A Problem-Solving Model Of Students Construction Of Energy Models In Physics

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE -
UNIVERSITÉ LYON 2
UMR Groupe de Recherches sur les Interactions Communicatives (GRIC), Equipe COAST
Université Lumière Lyon 2, 5 ave Pierre Mendès-France, 69676 Bron cedex 11, FRANCE.
Tél. (+33).4.78.77.31.17 Fax. (+33).4.78.77.31.15
Email (secr.). Francoise.Puthon@univ-lyon2.fr
rapports recherche
de
C O A S T
research reports
Diana Bental
1994
Rapport no. CR-7/94
Communication
et
Apprentissage
des
Savoirs
Scientifiques
et
Techniques
A problem-solving model of
students' construction of
energy models in physics

A problem-solving model of students' construction of
energy models in physics
DIANA BENTAL*
Modelling is an essential part of physical sciences, especially in physics.
In order to solve physics problems, students must relate objects and events in an
experimental or real-world situation to objects and events in a theoretical model. We
see this as a modelling problem. We aim to gain a deeper understanding of how
students construct models of physics.
One key aspect of modelling is the formation of links between the formal parts of the
model and the experimental situation that is to be modelled. Novices mostly construct
problem-solving representations from objects and events in the experimental situation
whereas experts construct representations that are closer to the theoretical entities.
More specifically, modelling requires (at least) that the modeller can determine which
real-world items are to be repreented in the model, which aspects of those items are to
be represented and which components of the model should be used to represent those
items or aspects of the real world. Representation of some items may require
intermediate abstractions from the experiment, abstractions that are not part of the
formal theoretical model as given but are still needed to build the model. Experts may
readily use these abstractions, but novices may lack these abstractions and may find it
difficult to form them.
ModelCHENE is a problem-solver which models the way that students learn to form
links between a formal theoretical model (of energy) and a physics experiment. The
problem-solver is given a description of a theoretical model (specifically, a model of
energy) and of a specific experimental set-up. It creates a model of thatexperiment (an
energy chain).
ModelCHENE is capable of modelling variations in the ways that the students go about
solving the problem and variations in the solutions that are eventually produced.
ModelCHENE explains these variations in terms of the knowledge that the students
bring to bear on the problem, the abstractions that the students can make (or don't
make) from the experimental situation, and the sources of knowledge that they refer to.
* Visiting researcher at COAST, April-October 1994

The first part of the report provides some context for ModelCHENE. Section 1
explains the larger research interests within COAST of which ModelCHENE forms a
part, Section 2 gives an overview of ModelCHENE. Section 3 describes the
methodology that we have used to fit into the research context and Section 4 describes
the specific requirements for the problem-solver.
The second part of the report describes ModelCHENE's architecture. This part of the
report combines high-level theoretical issues with pointers to the specific structures that
implement them. Section 5 describes the division of knowledge into problem-solver's
knowledge has been divided into different domains, the form of knowledge within each
domain and the connections between those domains. Section 6 talks about the
representation of energy. Section 7 describes the initialisation for modelling different
experiments. Section 8 describes the problem-solving processes.
The final part of the report presents results and implications of this work. Section 10
describes the role of intermediate abstractions within problem-solving for energy
chains. Section 11 gives results. Section 12 describes the implications for future
research of the language in which CLIPS is implemented. Sections 12 and 13 describe
future research with ModelCHENE and conclusions.
1. Background
There is a programme of research within COAST which explores the issue of how to
organise the teaching of physics, with the teaching of energy as one specific goal
(Tiberghien 1994). A particular teaching sequence is followed, in three phases:
Phase 1 Text Construction
The students first construct a circuit (battery, bulb and wires) using a charged battery
and then a flat battery. They write a text that describes the experiment. Next, they
categorise the statements in their texts into "observables and events", "electrical
circuits" and "other". The students notice that the "other" category typically includes
statements about energy.
The teacher provides the "seed" of the theory-model, information about energy as
shown in Table 1.
Phase 2 Construct Meaning for the Theory/Model
The students develop their understanding of the theory-model by using the energy
model to build energy chains that correspond to a sequence of experiments.

Experiment 1 A circuit consisting of a battery, bulb and wires.
Experiment 2 An object hangs on a string which is completely rolled round the axel of
a motor. A bulb is connected to the terminals of the motor. The object is allowed to
fall. The motor acts as a dynamo and the bulb lights.
Experiment 3 A battery powers an electric motor. A string is tied to the axel of the
motor at one end and to a weight at the other. The string is completely unrolled to begin
with. The circuit is closed. The motor turns and the string winds up, raising the weight.
Phase 3 Refine the Theory/Model and extend the field of applicability
The students explore the use of using quantitative values and experiments with 2
variables, energy and power.
Table 1. Information and rules for energy chain construction provided to students
Energy can be characterised
• by its properties:
• Storage
a reservoir stores energy
• Transformation
a transformer transforms energy
• Transfer
Between a reservoir and a transformer, or
between two reservoirs, or between two
transformers, there is energy transfer. The
different modes of energy transfer of a
system to another one are: work, heat and
radiation.
• by a fundamental principle of
conservation
Energy is conserved whatever its
transformations , its transfers or its forms
of storage
To build an energy chain you have to use
those symbols and take into account
those rules:
Reservoir
Transformer
Transfer
• a complete energy chain starts and ends
with a reservoir
• under each rectangle indicate the
corresponding object (or the objects) in
the experiment
• under each arrow indicate the mode of
transfer. If there are several modes of
transfer use one arrow for each mode of
transfer (between two rectangles).
Our research focuses on the second phase, developing an understanding of the theory-
model by building energy chains. CHENE is a modelling environment for students, an
environment which is designed to support the development of the idea of an energy
chain and associated concepts such as power.
CHENE is typically used as follows. Students are supplied with a description of a
thereotical model of energy, in terms of reservoirs, transformers and transformers

(shown in Table 1) The students, working in pairs, are then given the task of building a
sequence of energy chains. Each energy chain describes a given experimental situation.
The first example is a battery and a bulb which have been connected to make the bulb
shine. After they have modelled the battery and bulb, the students are shown an
energy chain created by an "expert" for that experiment. They then go on to model the
two further experiments. The sequence of experiments is devised to raise new
questions about the meaning of the theory and model, thus deepening the students'
understanding of the theory and mode and their application to the real world.
Figure 1. Energy chains built for each experiment by an expert
Experiment 1 : Battery-Bulb
Battery
Reservoir
Electrical
Work
Transformer Heat
Radiation
Environment
Reservoir
Bulb
Motor
Electrical
Work
Transformer
Heat
Radiation
Environment
Reservoir
Bulb
Weight +
Earth
Reservoir
Mechanical
Work
Transformer
Experiment 2 : Falling Weight
Battery
Reservoir
Electrical
Work
Transformer
Motor
Mechanical
Work
Weight +
Earth
Reservoir
Experiment 3 : Rising Weight
Figure 1 shows the energy chains produced by three pairs of students for the first
experiment, the battey and bulb. Figure 2 shows the energy chain produced by the
expert for the same experiment. There are differences in the chains produced by the
different pairs of students, and between all the expert's solution and the students'.
The students use CHENE to build each energy chain. CHENE supports the students in
learning about modelling energy chains. It also supports research. Transcripts are taken
of students' dialogues during their sessions with CHENE, and their interactions with
CHENE are noted automatically. The interface to CHENE allows the researchers to
monitor the external sources of information that the students refer to and the operations
that students use to create the energy chain.

Figure 2. Energy chains built by students for the battery-bulb experiment1
Reservoir
Reservoir
Penny-Frederic Fiona-Lawrence Daniel-Susan
Battery Bulb
Transformer
transfer
the carrying
carrying wires
light
rays
Heat eyes
human
body
Battery
Battery
Reservoir
Bulb
Bulb
Transformer Transformer
Transformer
Transformer transfer
wires
(electric current)
Heat
Return
of energy
Arrival
of energy
Detailed analysis has also been done of the transcripts obtained from CHENE, to
categorise the sources of knowledge and mental operations being used (Megalagaki and
Tiberghien 1994) . ModelCHENE is a formal system that models aspects of students'
problem-solving with CHENE. ModelCHENE is intended to provide insights into the
reasoning processes that underlie their problem-solving. We have drawn on some of the
conclusions of this analysis for our modelling. ModelCHENE at present uses a less
detailed categorisation than is given by this research. Other research within COAST
has considered the interactions between the students as they work in pairs, and the
effect that working on pairs has on their learning (Baker 1994, Georgin 1994). It is
intended that eventually the ModelCHENE problem-solver should be able to fit into a
dialogue model, with two problem-solvers each working on the same problem, each
bringing different knowledge to bear, and building the model as a co-operative
exercise, as in the real experiments.
2. ModelCHENE 3 Overview
ModelCHENE is a problem-solver which models that way that students form links
betweena formal theoretical model of energy and a physics experiment. The problem-
solver is given a formal description of the theory / model and the experimental field,
and builds an energy chain for that experiment.
ModelCHENE models formally the knowledge that students bring to bear on the
problem and the way that this knowledge is used. It accounts for aspects of the form of
the energy chain, the order in which the chain is created, and for thebeliefs that
students express during the dialogue (as revealed by the protocol analyses).
1The students' names have been changed.

ModelCHENE builds energy chains for three experiments, battery-bulb, falling-mass
and rising-mass. These energy chains are for an ideal "abstract" student. For the first
battery-bulb experiment, ModelCHENE also models some variations from this "ideal",
and it accounts for some of the variations in problem-solving between different student
pairs.
ModelCHENE is implemented in CLIPS 6.0 on a Macintosh. CLIPS is an expert
system shell that offers a rule language, inference engine, the ability to build object
structures and object-oriented programming. We used CLIPS' object structure to
support the division of knowledge into different domains and the rules to implement the
reasoning processes.
ModelCHENE accounts for problem-solving in terms of:
- different knowledge domains that students use and relate together during
problem-solving;
- the formation and use of intermediate abstractions;
- the use of specific rules and very general operators;
- different phases of reasoning during problem-solving.
3. Methodology
ModelCHENE is part of a continuing research effort on how students learn to solve
physics modelling problems. It is therefore important that ModelCHENE should be able
to adapt to include developin theories and explore their consequences for problem-
solving. In this research context, we decided that a prototyping methodology was most
suitable for ModelCHENE. ModelCHENE has existed as a series of prototypes. At all
stages there was a "working" problem-solver in existence, each new prototype having
some new or altered features. In early versions, certain aspects of the problem-solving
have been ignored or modelled only in the most general terms. Where we did not have a
theory for how particular parts of the chain were built, we simply put those parts in
place. Subsequent versions have expanded these aspects in more detail, developing
theories about how the energy chains are built in discussion with the other researchers
in didactics and protocol analysis within COAST. This is akin to the methodolgy
suggested for building systems that learn in SOAR (Ritter and Larkin, 1994). To begin
with, the computer system "just knows" how to do certain things. Later on, the system
models how the student comes to know those things. This method has enabled the
development of ModelCHENE to interact with the detailed analysis of protocols being
undertaken at the same time.

Prior to the work described in this paper, Roshni Devi had developed a simple rule-
based problem-solver which modelled some aspects of problem-solving in the CHENE
context (Devi 1993, Devi et al 1994). Devi's model successfully models several
specific incidents within the students protocols. However the psCHENE architecure
was not able to support an in-depth study of student learning (Devi et al 1994).
psCHENE relied on an unstructured verbal representation of the students' knowledge.
psCHENE focussed on modelling specific incidents in the dialogues rather than the full
task of building and labelling the entire energy chain. It represented only a few aspects
of the energy chains and experimental field. It used a hard-wired inference engine to
model students' focus of attention.
To further our research into students' learning, ModelCHENE improves on psCHENE
in various ways. ModelCHENE includes an explicit model of the students' problem-
solving activities in terms of changes in formal structures rather than verbal entities. In
particular, ModelCHENE distinguishes explicitly between the different kinds of
knowledge being used, it models the formation of relationships between those different
kinds of knowledge, and it distinguishes between different kinds of activities in the
problem-solving. ModelCHENE represents in detail the entities in the experimental
field, their connections and their properties. It represents in detail the items in energy
chain, their connections and their labels, and it models the processes of forming and
labelling each entity in the energy chain. ModelCHENE contains an explicit
knowledge-based model of the students' focus of attention, instead of "hard-wiring" it
into the inference engine.
There have been 3 main versions of ModelCHENE.
ModelCHENE 1 concentrated on representing clearly the distinction between different
kinds of knowledge. It contains separate representations of the experimental field
knowledge, electrokinetic knowledge and the theory-model knowledge. ModelCHENE
1 is capable of accepting a description of the experimental field for different
experiments, creating a complete energy chain, and printing out the completed energy
chain. ModelCHENE 1 was able to model how some parts of the chain were derived
from physical objects in the experimental field. The issue of how the abstract items
such as energy and the environment are put into the energy chain was finessed.
ModelCHENE 1 simply put appropriate reservoirs, transfomers or transfers in the right
place in the chain for these items. ModelCHENE 1 solves only the battery-bulb
experiment and models only the behaviour of the "ideal" student.

ModelCHENE 2 began to model the problem-solving process in detail. It creates a
complete a complete energy chain using domain-specific rules and general operators.
ModelCHENE 2 can create a complete energy chain by inference. ModelCHENE 2 was
able to create an "ideal" energy chain for all three experiments. It has just one way to
focus attention, reaoning from the observable elements of the experimental field (via
some abstractions) to the energy chain.It incorporates the idea of different abstractions
being used for energy, only some of which were useful for building the energy chain.
ModelCHENE 3 is the current version. It distinguishes a further domain of knowledge,
that of intermediate abstractions which are neither part of the observable experimental
field nor part of the theory/model for energy. It includes three distinct phases of
reasoning, to allow for reasoning not just from the experimental field to the energy
chain (as in modelCHENE 2), but also critiquing of the energy chain in terms of the
theory/model and attention to other abstractions or aspects of the experimental field as a
result.
ModelCHENE 3 has two different models of focus of attention. Both are driven by an
initial focus on objects in the experimental field, and one is also driven by a form of
linear causal reasoning. ModelCHENE 3 incorporates a sequence of representations for
energy which are necessary before transfers can be created. ModelCHENE 3 models
different students' problem-solving behaviour for the first experiment.
4. The Theoretical Requirements for ModelCHENE
The objective of ModelCHENE is to explore hypotheses about what underlies some
relevant aspects of the students' behaviour in forming the energy chain. A problem-
solving model based on those hypotheses should be able to reproduce the interesting
behaviour.
There are three important aspects of the students' behaviour in forming the chain:
- the final chain itself;
- the sequence of operations that go in to making the chain;
- the conversation between the students while making the chain.
All of these are manifestations of the students' mental problem-solving processes when
building the chain. Our objective within ModelCHENE is to reconstruct and model
formally the mental problem-solving processes that could account for the final chain
and for the sequence of operations that produce it. We use information from the
dialogues in order to gain insight into the students' problem-solving processes
(Megalagaki and Tiberghien 1994).

ModelCHENE contains a general model of students' problem-solving, but one which
can also account for some important variations between different students' problem-
solving. We aim to account for some aspects of what the students are learning while
they solve the problem.
More spcifically, we aim to account for the form of the energy chain that is finally
produced, the way that the energy chain is labelled; and the order in which particular
elements of the chain are created. Both of these vary for different students. We account
for them in terms of the different knowledge that different students bring to bear on the
problem and the different operations that students perform on that knowledge.
The form of the chain consists of which reservoirs, transformers and transfers are
placed in the final chain and how these elements are connected together. We make the
following observations about the form of students' energy chains:
- students model the battery and the bulb correctly as a reservoir and a transformer
respectively, connected by (at least) one transfer pointing in the right direction;
- students fail to represent some transfers, or create inappropriate transfers;
- students fail to represent the environment (in the first experiment, at least).
There are various different kinds of labels assigned to the different elements of the
chain. Reservoirs and transformers each have to be labelled with one or more physical
objects in the experiment. Transfers should be labelled with one of four specific
"modes" of energy transfer. We make the following observations about the labelling of
students' energy chains:
- students typically label correctly the reservoirs and transformers for the battery and
bulb;
- transfers are typically labelled incorrectly, not with modes of energy transfer as
requested but with physical objects, informal terms or words from other physics
knowledge;
- the only pair of students who invented some entities that might correspond to the
environment did some creative labelling ("body" and "eyes").
(From our perspective, the question of whether an element in the chain is a reservoir or
a transformer is treated as a matter of form rather than labelling.)
In terms of the sequence of operations, the specific aspects of performance that we want
to model are:
- the battery is quickly identified as the first element in the chain;

the battery is fairly easily identified as a reservoir and the bulb is identified as a
transformer;
- students spend a lot of time (comparatively) discussing energy transfers;
- in the first experiment, students identify singly each entity in the experimental field
with a single entity in the theory/model. (In subsequent experiments, students make this
identification in larger sections, identifying whole chunks of the experimental field with
whole segments of the energy chain in a single operation );
- students use observations about the experimental field to determine the energy
chain;
- students also use the constraints of the model to determine what to look for in the
experimental field.
We hypothesise that what is underlying these behaviours are that the students:
- students lack the right abstractions and find it easier to deal with concrete objects;
- students need to apply more reasoning to deal with (abstract) energy transfers than
with physical items;
- students apply detailed common-sense knowledge to the experiment;
- students use a few simple operators for mapping the experiment onto the
theory/model, and especially the use of property matching;
- students apply linear causal reasoning.
If our hypotheses are correct, we should be able to model these preferences and
reasoning processes, and from them we should be able to obtain similar energy chains
as those the students build, and in a similar order.
5. Knowledge domains
Two key aspects of problem-solving are the use of different kinds of knowledge and the
formation of relationships between those different kinds of knowledge.
We have divided the knowledge that the students use into four domains:
- theory/model
- experimental field
- electrokinetic
- intermediate abstractions.
The theory/model contains knowledge about the reservoirs, transformers and transfers
that can appear in the energy chain. The experimental field contains knowledge about
the physical items that form part of the experiment, for instance the battery, bulb and
wires in the first experiment. The intermediate abstractions domain contains knowledge
about conceptual items that are neither part of the formal theory nor part of the directly

observable experimental field, such as the "environment" around the experiment and
"energy". (These abstractions may be sophisticated or naive.) The electrokinetic domain
contains knowledge about items associated with electricity, especially the notion of a
circuit.
Within these four knowledge domains, we formalise knowledge into
entities
classes
properties - property slots and values
relations - relational slots and values
Entities are individual, specific items - for example, the battery in experiment 1, or the
reservoir that corresponds to that battery. Each of the two wires in experiment 1 is a
separate entity. (Entities in our model are implemented as CLIPS instances). Entities
may correspond to a physical entity (like a battery) or to a conceptual entity which may
or may not have a physical manifestation (like a particular reservoir, which is a
conceptual entity that may be drawn on a page or created within CHENE).
Classes are a generalisation of entities. There is a general class of batteries, of
reservoirs, of wires. There are four separate hierarchies of classes and sub-classes, one
for each knowledge domain. So the theory-model domain contains classes for
reservoirs, transformers and transfers; the experimental field contains classes for bulbs,
batteries, wires, motors and so on. Classes are organised in a hierarchy. At the top of
each class hierarchy is the knowledge domain; at the bottom are classes for which we
may have specific entities. (Classes in our model are implemented as CLIPS classes)
The battery in experiment 1 is an entity in the general class of batteries; the reservoir
that corresponds to the battery is an entity in the class of reservoirs; the reservoir that
corresponds to the environment in experiment 1 is a different entity in the same general
class of reservoirs.
Each entity has a number of properties. These properties enable the problem-solver to
distinguish between, and reason about, different entities. For example the fact that a
bulb is shining is represented as a slot called observational-properties in the bulb entity,
a slot which has the value shining. (Properties are implemented in CLIPS as slot
values). Slots describe the range of possible properties that each entity in a class may
have. Slots form part of the description of the class, and all entities in a class have the
same slots. For example, the fact that reservoirs may have some inputs is represented by
a slot called inputs in the class of reservoirs.

Relations describe the logical and physical connections between different entities, and
between entities and classes. One key relationship is the class and superclasses to which
an entity belongs. For example, a reservoir entity belongs to the class of reservoirs,
which in turn belongs to the superclass of nodes in the energy chain, which in turn
belongs to the theory-model knowledge domain. (An entity's membership of a class,
and relationships between class and subclass are implemented by the is-a and of
expressions which are built in to CLIPS.) Of equal importance are the relations which
connect entities from different classes. The fact that in the first experiment the battery is
physically connected to the two wires is represented by two relations, one between the
battery and each wire. These relations are represented within the object for the battery
(by a relational slot wires which contains the names of the wires) and also
(equivalently) within each of the wires (in a relational slot ends which contains the
name of the entities to which that wire is connected). (These relations are also
implemented in CLIPS as slot values. )
We focus on the way that students form relationships between different knowledge
domains. The domains are separate in that each class and each instance of a class exists
in a single knowledge domain. Relational slots allow objects in one knowledge domain
to refer to knowledge from a different domain. For example the problem-solver may
consider that a particular reservoir (which is an entity in the theory/model) in the
energy chain is to be used as a representation for the battery (which is an entity in the
experimental field). Relational slots also allow an entity in one domain to refer to a
class in another. For example the reservoir that represents the battery can also contain a
slot indicating that it represents something in the class of batteries.
Viewing this as a mental model of what the student knows, the student is assumed to be
aware of the properties of particular instances. So the student model is "aware" that a
battery stocks energy or that a bulb is shining. These properties may be properties of a
specific object (e.g. the bulb is shining, reservoir-1 is labelled "battery"). Some
properties are general knowledge about a class of objects (e.g. batteries stock energy)
which is then applied to a particular instance. Within the problem-solver this knowledge
is obtained by inheritance of properties from the general class of objects. (For certain
properties in the theory/model, general knowledge is represented as facts rather than
inherited properties. These are noted in the tables for the theory/model).
Students are also assumed to be aware that an object belongs to some classes. So the
student model is "aware" that a particular object is an instance of some classes - such as
that an object is a battery, that this object is something within the experimental field,
that it is something that can be found in a circuit. However students are not assumed to

be aware of the details of the class/subclass structure itself. Thus the structure of the
class/subclass structure itself is not used by the problem-solver.
The diagrams and tables in this section summarise the structure of each domain. The
diagram shows the class hierarchy for each kind of knowledge. The tables show the
slots and the slot contents for each class. Slots are inherited by sub-classes and by
instances in each subclass: I have ony shown them in the top class where they appear.
some slots can contain only one value, others can contain several values. Relationtal
slots are labelled with the word link..
Our model is somewhat similar to de Kleer's component model of electric circuits (de
Kleer 1984). We have a library of general components with which to make a circuit and
we select specific components from that library. Unlike de Kleer, we have different
libraries. One library is the theory-model, another the experimental field. These are both
models of the same circuit. Unlike de Kleer, our naive physics is not required to be a
consistent but simplified physics constrained by principles such as the "principal of
locality" and "no function in structure" (de Kleer 1984, de Kleer and Brown 1984);
instead our naive physics is constrained on the one hand by the theory-model and on the
other by students' beliefs, which do not necessarily obey these principles.
The Theory/Model
We do not tease apart the theory and model, but treat them as a single knowledge
domain that the students are trying to understand. We formalise the theory/model as
follows:
The theory / model consists of three classes of components: reservoirs, transformers,
and transfers. These have significant properties: reservoirs store energy and
transformers transform energy, Transformers have the modes electrical work,
mechanical work, heat and radiation.
An energy chain is a set of connected instances of components from the theory/model.
Each instance of a component has slots for the instances with which it is connected.
Reservoirs and transformers have inputs and/or outputs slots, which may contain the
identifiers of one or more transfers. Transfers have from and to slots, each of which
should contain the identifer of at least one reservoir or transformer.
Our objective is to represent relations between different knowledge domains as the
students learn to map between them, and especially as they learn to relate the

theory/model to other knowledge domains. So, an item in the energy chain may be
related to one other kind of item. This may be to a physical item in the experimental
field (e.g. batteries, bulbs) or to an abstraction from it (e.g. electricity, the environment).
These links represent both relationships to a specific item (e.g. reservoir-1 is
representing battery-1) and to a class of items (reservoir-1 is representing a battery).
These relational links are stored in the slots entity-label and entity-class respectively.
Finally, each item in the energy chain has a label written-label to represent the label that
the student will actually write onto that reservoir, transformer or transfer. These labels
are read from the experimental field or from the abstractions. Reservoirs and
transformers are normally be labelled by something in the experimental field, except for
the reservoir labelled the environment, wich is an abstraction. Tranfers may be labelled
by a mode of energy - but some students label transfers with physical items, and this
behaviour is also modelled.
Theory Model Items
Chain Nodes Transfers
Transformers
Reservoirs
Class of Items Slots What is stored in slots
Theory Model Items knowledge-domain theory-model
entity-label Link: Experimental Field
or Abstract Item
entity-class Link: Kind of
Experimental Field or
Abstract Entity
written-label An object-descriptor or a
mode-of-transfer

Reservoirs inputs Transfer(s)
outputs Transfer(s)
Transformers inputs Transfer(s)
outputs Transfer(s)
Transfers mode-of-transfer A Mode of Energy
transfers-from Chain node
(reservoir or transformer)
transfers-to Chain node
(reservoir or transformer)
Modes of energy transfer are: heat, light, electrical-work or mechanical-work.
Other information about elements of the theory model:
(not stored within the CLIPS object structure for implementation reasons)
Reservoirs store energy
Transformers transform energy
Transfers transmit energy
(Student Models 2 and 3 only)
Electrokinetic Knowledge
The structure for electokinetic knowledge is very simple at present. It contains only the
inforlation that elements are connected in a circuit. In terms of the student model, this
knowledge domain is a place-marker for the future, when it could include students'
electrokinetic knowledge obtained from their previous physics lessons.
Electrokinetic Items
EK circuits
Electrokinetic Items knowledge-domain electrokinetic
Circuits EF-circuit-parts Physical object(s) in the
circuit
EF-circuit-wires Wire(s) in the circuit
The Experimental Field

Students have to decide which physical entities and phenomena in the experimental
field have to be considered in the energy chain.
Students are often unclear about some aspects of the experimental field especially in the
first experiment. In particular, they need to resolve the scope of the experimental field,
the scale of objects within the experimental field, and which observables are relevant.
The scope determines which physical entities in the real world are considered to be
part of the experiment. Typically students start off by considering only those objects
that are physically contained within the experiment. One pair eventually considered
objects outside the experiment as well, that is, they treated their own eyes and bodies as
destinations for heat and light and therefore as objects to be represented within the
energy chain.
The scale determines whether objects are considered as a whole or in terms of their
components (de Kleer 84). For instance the battery-bulb circuit could be considered as a
single entity; or else it could be considered as a collection of entities (battery, bulb and
wires) which are connected in a particular way to form a circuit; or else the bulb itself
could be considered in terms of its glass, electrical connections, filament etc.
The observables are the entities in the experiment which the students refer to. This
includes the physical objects, and also other entities or properties which can be directly
observed, such as the presence of light and heat. Students may not in fact notice all of
these entities. The fact that the light bulb is shining may be obvious, but the fact that it
is hot may be less so. Students may even assume that something can be observed when
in fact it is not present .
For the purpose of our modeling, we have started from a single position on all of the
three issues. We have described the experimental field in terms that cover the objects
and properties that all the students used and we have avoided idiosyncratic ones (that
were used by only one student/pair). The scope of the experiment consists of the
entities within the experiment itself. The scale has been simplified to the set of
distinct objects within the experiment that were referred to by all of the students. For
instance the battery-bulb experiment is described in terms of a battery, two wires and a
bulb. The physical connections between the objects are also common knowledge to all
the students, and they are important factors in how the experiment behaves and how it is
understood. The model therefore includes the way in which these objects are connected
in each experiment, for instance that the battery is connected to two wires and that the
wires are in turn connected to the iught bulb. In addition to the existence and

connectivity of particular physical objects, we have treated as observables the fact that
these objects may move, shine and or be hot .
Before the problem-solving starts, the system asks which experiment is wanted.
Instances of the appropriate experimental field class are created (e.g. a battery, two
wires and a bulb for the battery-bulb experiment) and their connections are created. As
a result of this, values are given to the relevant observables.

The Object Structure for the Experimental Field
Experimental Field Items
EF circuit elements EF objects EF connectors
EF circuit objects
EF bulbs
EF batteries
EF motors
EF bobs
EF wires
EF strings

The Object Structure for the Experimental Field (contd)
Experimental Field Items knowledge-domain experimental-field
TM-label Link: Theory Model Item
TM-class Link: Kind of Theory Model
Entity
chain-role nil, first-cause or subsequent
EF-objects observational-property Properties that can be observed
(hot, shining, rising, falling)
contacts EF object(s) touching this one
topological-position Distance from the first-cause
gives Link: Intermediate Abstraction
for Energy
gets Link: Intermediate Abstraction
for Energy
EF-bulb in-circuit Link: Electrokinetic Item
wires Wire(s) connected to this object
object-descriptor bulb
EF-battery in-circuit Link: Electrokinetic Item
wires Link: Electrokinetic Item
stocks energy
object-descriptor battery
EF-motor in-circuit Link: Electrokinetic Item
wires Link: Electrokinetic Item
string String attached to this motor
object-descriptor motor
EF-wire transmits Link: Intermediate Abstraction
for Energy
ends EF objects touching this one
object-descriptor wire
EF-string transmits Link: Intermediate Abstraction
for Energy
ends EF objects touching this one
object-descriptor piece-of-string
EF-bob string String attached to this bob
stocks energy
object-descriptor bob-or-weight
Intermediate Abstractions
The abstractions that students make from of the experimental field largely determine the
form of the energy chain that is built. Different students have different perceptions of
the experimental field and they make different abstractions from the experimental field.
These abstractions are then mapped onto the theory/model. These differences lead to
different students building different energy chains. The results of their abstraction
process become part of the students' perception of the experimental field (and might be
expected to become part of their perception of a similar set-up in a new experiment).

The Object Structure for Intermediate Abstractions
Intermediate Abstractions
IA energy IA environment
IA heat
IA light
IA electricity
IA movement
Intermediate Abstractions knowledge-domain intermediate-abstractions
TM-label Link: Theory Model Item
TM-class Link: Kind of Theory
Model Entity
chain-role nil, first-cause or
subsequent
IA-energy
(also IA-heat, IA-light,
source Link: Experimental Field
Entity
IA-electricity and
IA-movement)
destination Link: Experimental Field
Entity
IA-environment stocks energy

The environment is an abstraction that has some of the same properties as an item in the
experimental field.

6. Representations for Reasoning About Energy
ModelCHENE 3 is designed round a hypothesis about how energy is understood. Our
hypothesis is that students' awareness of energy has four forms, which we summarise
as:
* observational properties
Observational properties are the observable aspects of some object. Any physical object
in the experimental field might have such properties. The exact properties that are
created depends on the experimental set-up. The possible set of propositional properties
in the system at present is:
the bulb is shining (object EF-bulb-1 (observational-property shining))
the bulb is hot (object EF-bulb-1 (observational-property hot))
the bob is rising (object EF-bob-1 (observational-property rising))
the bob is falling (object EF-bob-1 (observational-property falling))
These properties are part of the initial set-up of the system.
* energy properties
Physical objects in the experiment field can give or get some form of energy. Even if
the student is not sure that whatever is being given or received is really a valid form of
energy, the notion of gives and gets still applies. Give and get are local to a single
object in the experimental field.
The set of possible predicate properties is:
the bulb gives light (object EF-bulb-1 (gives light))
the bulb gives heat (object EF-bulb-1 (gives heat))
the bob gets movement (object EF-bob-1 (gets movement))
the bob gives movement (object EF-bob-1 (gets movement))
the bulb gets electricity (object EF-bulb-1 (gets electricity))
the battery gives electricity (object EF-battery-1 (gives electricity))
the motor gives electricity (object EF-motor-1 (gives electricity))
the motor gets electricity (object EF-motor-1 (gets electricity))
the motor gives movement (object EF-motor-1 (gives movement))
the motor gets movement (object EF-motor-1 (gets movement))
There are three other energy properties, stores, transforms and transmits. Batteries
and bobs may store electricty and movement respectively, while wires (and only
wires!) may transmit electricity. The environment also stores energy. The system may
infer that bulbs and motors transform energy.

Gives and gets properties may be part of the general knowledge about a class of
entities, or they may be derived from other knowledge (e.g. from observational
properties) by rules. stores is part of general knowledge about batteries and about the
environment, whereas transforms is derived from other knowledge (i.e. from gives and
gets). The transmit property of wires is part of the background knowledge for problem-
solving Models 2 and 3 for the battery/bulb experiment. It is not used by any other
problem-solving model.
The energy properties stores and transforms are important because they are also
properties of reservoirs and transformers in the theory / model. Gives and gets
properties are also important because they are used to derive energy relations, from
which energy transfers can be derived.
* energy relation
Two physical objects in the experimental field may be connected by an energy relation.
The relation is directed, e.g. the battery gives electiricty TO the bulb.
These are implemented by a three-place relation
(energy-relation <type> <from-object> <to-object>)
So the following energy relations are created (depending on the experiment and the
model):
the bulb lights the environment
(energy-relation light EF-bulb-1 EF-environment-1)
the bulb heats the environment
(energy-relation heat EF-bulb-1 EF-environment-1)
the bob moves the motor
(energy-relation movement EF-bob-1 EF-motor-1)
the motor moves the bob
(energy-relation movement EF-motor-1 EF-bob-1)
the battery supplies electricity to the bulb
(energy-relation electricity EF-battery-1 EF-bulb-1)
the motor supplies electricity to the bulb
(energy-relation electricity EF-motor-1 EF-bulb-1)
* energy process
Energy is treated as a process in own right, with a source, a destination and a type.
e.g.

(object EF-electricity-1 (is-a electricity EF-energy)
(knowledge-domain intermediate-abstractions)
(source EF-battery-1)
(destination EF-bulb-1))
The key point is that each energy process that is created exists within the knowledge
domain of intermediate abstractions . That is, enough is known about it to create an
energy transfer. However these abstractions are still rather informal: they are treated in
terms of elecectiricty or light rather than the formal modes of energy transfer in the
theory-model, electrical work or radiation.
We could argue that energy relations themselves should exist within the intermediate
abstractions: however at present the energy process doesn't actually contain any more or
any different information than the energy relation. An energy process is always created
whenever an energy relation is created. In future, when energy processes are more
developed, energy relations themselves may need to be treated as separate intermediate
abstractions in their own right, and transfgers could be created directly from energy
relations rather than energy processes.
There is no special need that all kinds of energy transfer should be easily represented at
all four stages. For example there is no observable property of batteries from which the
model (or indeed the students) derive the fact that a battery stores energy. That is
simply a part of their general knowledge about batteries. Only in order to create a
transfer as required, the students must get to the fourth stage! We hypothesise that the
differences between how different kinds of energy are percieved could explain some of
the different reasoning steps that are used.
7. Initialisation: Setting up Different Experiments
ModelCHENE 3 can model three different experiments. The different experiments are
set up by creating instances in the experimental field.
The switch for the different experiments is set by one of three different facts.
(experiment is battery-bulb)
(experiment is falling-mass)
(experiment is rising-mass)
All three experiments are independent of each other. The problem-solving strategy for
all three experiments is always the same. We do not model the application of results

learned during the first experiment, nor do we model other reasoning strategies that
students bring into play for the later experiments.
The user is asked which experiment is wanted and these facts are set up and maintained
by rules
util-get-experiment-1
util-get-experiment-2
util-get-experiment-tidy
and the starting facts experiments-facts.
The setting up is done by the init rules. The first three setting up rules create instances
for the physical objects in the EF. Each rule matches on an (experiment is ...) fact
and it creates the appropriate experimental field instances. For example, for the
battery/bulb experiment, the rule init-battery-bulb-objects-EF creates a battery, a
bulb and two wires in the experimental field. The rules init-rising-mass-EF and
init-falling-mass-EF do the same for the other two experiments.
Other rules create the connections between the objects in the experimental field: init-
connect-motor-to-bob is used in the second two experiments and init-connect-two-
objects-with-wires is used in all three experiments. The latter rule relies on the fact that
all three circuits contain just two objects and two wires although the objects being
connected are different in all three experiments.
The rule init-connect-two-objects-with-wires creates an instance of a circuit in the
electrokinetic knowledge domain and it notes which EF objects and wires are connected
in the EK circuit. The rules init-complete-circuit-information-1 and -2 create the
opposite pointers to the EK circuit instance from within the relevant EF objects and
wires (mainly for convenience).
Finally in the setting up phase, there are three rules which create the observable
consequences of the experimental set-up. init-bulb-in-circuit states that if a battery and
a bulb are in the same circuit, then the battery is lit and hot; init-battery-motor-and-bob-
in-circuit declare that if a battery and a motor are connected in a circuit and the motor is
tied to a bob, then the bob is rising; init-bulb-motor-and-bob-in-circuit declare that if
the bulb and motor are in a circuit, if there is no battery in the circuit and the motor is
attached to a bob then the bob is falling and the bulb is lit. (nb but not that the motor
turns).
There are two important points about these three final rules. First, they are specific -
they don't represent a general theory of motion or electricity. They don't represent

anything that the student knows, either - they just conveniently create a situation that
the student is going to observe and reason about.
(This differs from ModelCHENE 2b. At this stage in ModelCHENE 2b, some energy
knowledge was already represented and therefore available to the student. But in
ModelCHENE 3b, only observable properties are represented at this stage.)
So, at the end of the setting up phase we can state that the student knows that:
For the battery-bulb experiment:
- there is a battery, a bulb, and some wires (EF);
- these elements are connected up in a circuit (EF/EK);
- the bulb is shining and hot (EF).
For the rising-mass experiment:
- there is a battery, a motor, some wires, a bob and a string (EF);
- the battery, motor and wires are connected up in a circuit (EF/EK);
- the motor is tied to the bob by the string (EF);
- the bob is rising (EF).
For the falling-mass experiment:
- there is a motor, a bulb, some wires, a bob and a string (EF);
- the bulb, motor and wires are connected up in a circuit (EF/EK);
- the motor is tied to the bob by the string (EF);
- the bob is falling (EF);
- the bulb is shining and hot (EF).
nb the fact that the motor is turning is not represented explicitly.
ModelCHENE 3 can build "ideal" energy chains for all three experiments. Individual
variations in problem-solving are modelled ony for the first experiment.
8. The Problem-Solving Process
The ModelCHENE problem-solver starts off with general knowledge (i.e. class
structures) for all four knowledge domains. Before problem-solving starts, the problem
solver is also given information about which specific entities exist in the experimental
field, about how they are connected together and about which properties these entities
have. The problem-solver may also be pre-supplied with some entities that are

intermediate abstractions. The objective of the problem-solver is to use this information
to build an energy chain. That is, to form a set of entities in the theory model belonging
to the right classes, with appropriate labels on those entities, with the right connections
between entities in the chain and and the right connections between those entities and
the experimental field. As the problem-solver progresses, some further entities for
intermediate abstractions may be formed.
We can compare this with the students' problem-solving. The students use observations
about the specific experiment, plus background knowledge about the items in the
experiment, general knowledge and physics knowledge. They may bring some useful
knowledge about particular abstractions to the problem. The students combine this with
the information supplied about the theory/model to produce a specific instantiation of
the theory-model - that is, an energy chain for the experiment. Students may create
useful abstractions while they solve the problem.
We have begun by modelling problem-solving for the battery-bulb experiment. The
features that distinguish problem-solving in the first experiment are:
- reasoning is mainly in the form of matching between individual elements of the
theory-model and items in the experimental field (as compared to later experiments,
where matching takes place between larger chunks of the experiment and chain)
- students typically begin by searching for a "first cause" and reasoning along the
objects in the experiment from the "first cause".
ModelCHENE has three distinct phases of problem-solving:
- forward reasoning
- checking
- correction.
The forward reasoning phase reasons about the experiment and creates abstractions and
energy chain elements from the experiment. The checking phase checks the energy
chain so far against the criteria specified in the theory-model. The correction phase
proposes "fixes" to the energy chain to make it fit these constraints.
These phase correpond loosely to some general phases in problem-solving which are
common to most students. These steps don't necessarily appear in all the protocols or in
the same order, but their existences makes a useful generalisation which can be varied
for individual students. These phases include:

build up relations between real objects and the theory, and use this to get started (e.g.
note that the battery is a reservoir);
- get stuck and search around in all sources of information for a way to proceed
(experimental field, problem specification, common sense, ask each other....);
- run out of things to do;
- check the problem specification to see if anything's been left out or is not consistent;
- look at the experiment to see if anything has been left out;
- put more things in or change things to satisfy the specification;
- get conflicts between the two students (at any point!), argue and explain.
Figure 3 presents a task analysis of ModelCHENE's main problem-solving activities.
The forward reasoning consists of all tasks that are not checking and correction. Each
task is described in more detail in the following sections.

START
Determine energy
properties ofEF1
? Pair EF1, EF2
giver/receiever?
? EF1 has a
matchable energy
property ?
Establish energy
process / relation
IA1 (EF1 <--> EF2)
Establish TM item
which corresponds to
IA1 or EF1
TM1
IncorporateTM1 into
energy chain
Choose an EF Item
EF1
LabelT M1
gives
gets
stores
transforms
transmits
? Energy Chain Complete ?
? Energy Chain Correct ?
Propose
Corrections
FINISH
Checking
Figure 3: Task
Analysis

Forward reasoning phase
In the following discussion, we use the example of the abstract "ideal" problem-solvng
model for the battery-bulb experiment.
The setting up phase has already created some observable information that should lead
to inferences about energy. These are represented as properties of objects in the
experimental field. The bulb has the obseravtional properties hot and shining. In the
setting-up phase, one further piece of information is not represented by properties but
by electrokinetic knowledge. The setting up phase has also created the fact that the
battery/bulb together with the wires, make up a circuit.
Choosing an Experimental Field entity
ModelCHENE has two possible strategies for choosing an EF entity. Both are driven by
an initial focus on objects in the experimental field, and one is also driven by a form of
linear causal reasoning. Assertions of the form
(operation find-represention <EF-entity>)
are used for this.
If linear causal reasoning is not selected, then any entity may be chosen. The rules
flat-control-start
flat-control
assert that a representation is to be found for all objects, in no particular order, e.g.
(operation find-represention EF-bulb-1)
(operation find-represention EF-battery-1)
(operation find-represention EF-wire-1)
If linear causal resoning is selected, then the choice follows a particular sequence:
First ModelCHENE attempts to identify a "first cause". An object which gives energy
and does not get energy is a candidate for a "first cause". (The battery is chosen).
linear-control-start
linear-control-first-cause-activator
linear-first-cause-success
linear-first-cause-tidy-1, 2, 3
When a "first cause" has been identified, ModelCHENE linearises all the entities in the
circuit starting from the first cause. This is done by assigning an integer value to the slot
topological-position for each object in the experiment, starting from the battery with

value 0. The wires are each assigned position 1, the bulb position 2. (Positions are
assigned breadth-first follwing the contacts slots. CLIPS is switched into breadth-
first mode to do this).
linear-direction-first-cause
linear-direction-next
linear-direction-end
Finally ModelCHENE has a set of rules which reason about each object at a given
distance from the first cause. Reasoning starts at the first cause and all objects at a given
topological-position are analysed before ModelCHENE moves on to the next. These
rules are
linear-control-1,2,3
linear-chain-role
The order for the battery-bulb experiment using liner causal reasoning is:
(operation find-represention EF-battery-1)
(operation find-represention EF-bulb-1)
Determine Energy Properties of an Exprimental Field entity
ModelCHENE has rules which focus control on particular energy properties of
particular objects. These rules enable the rules that determine gives and gets energy
properties. (In a future fully goal-based implementation, we might have rules to focus
control on other energy properties and other apsects of reasoning.)
focus-gives
focus-gets
focus-tidy
Rules that reason from propositional properties:
prop-rising-bob A rising bob gets movement
prop-falling-bob A falling bob gives movement
prop-shining-bulb A shining bulb gives light
prop-hot-bulb A hot bulb gives heat
Rules that use connectivity and observational properties:
prop-motor-raises-bob
If a motor is attached by a string to a rising bob then the motor gives movement
prop-bob-turns-motor
If a motor is attached by a string to a falling bob then the motor gets movement
Rules that use existing energy properties, connectivity and electrokinetic knowledge:

prop-electrical-source-1
An object that stores energy and is in a circuit gives electricity
prop-electrical-source-2
An object that gets energy (not electricity) and is in a circuit gives electricity
The first of these rules has priority.
prop-electrical-destination
If an object that gives electricity is in a circuit
with another object that gives some other energy (not elctricity)
then the second object gets electricity
Finally we have a rule which uses the gives and gets properties to infer the transforms
property:
property-transforms-1
If an object gives one kind of energy
and gets a different kind
then that object transforms energy
In the battery-bulb experiment for the ideal student, the battery has stores property to
begin with andthe gives property is inferred; gives, gets and transforms properties are
inferred for the bulb. No properties at all are inferred for the wires.
Matching Energy Properties (for Reservoirs and Transformers)
We have rules which focus the attention of the problem-solver on particular properties:
property-transforms-1
property-stocks-1
property-stocks-2
Reservoirs and transfomers are created by a general property matching operator.
Property matching reasons that if an entity in the experimental field has the same
property as some class in the theory model, then the ientity in the experimental field
should be represented in the theory model by an instance of that class. So, if real-world
batteries stock energy and reservoirs also stock energy, then the student reasons that a
battery should be represented by a reservoir.
The property matching uses a set of relations on classes, like this:
(deffacts matchable-properties-facts
(matchable-property theory-model reservoir
stocks energy)
(matchable-property experimental-field EF-battery
stocks energy)
(matchable-property experimental-field EF-bob
stocks energy)
(matchable-property theory-model transformer
transforms energy))

The key rule is
OP-match-object-properties
This looks for an instance in the experimental field, looks at the class of that instance
and then checks against these matchable-property facts for the class. Given an instance
of EF-battery EF-battery-1, the relation states that EF-batteries stock energy and so do
reservoirs. Hence the rule creates an instance of reservoirs in the TM and that it
represents EF-battery-1. (Strictly speaking this rule does not create the TM instance
directly; instead it creates a goal that the TM instance should be created).
Property matching implies that an entity in the energy chain may be created for an
entity within the EF domain if the TM class has just one property in common with the
EF object.
The fact that batteries and bobs stock energy is treated as prior knowledge, something
that the student is assumed to "just know". The knowledge that motors and bulbs
transform energy is treated as an inference that the student can work out. The rule
property-transforms-1 asserts a matchable-property fact for bulbs or for motors, as
appropriate. Once this inference is made for bulbs or motors, the rule OP-match-object-
properties can use property matching to create the transformer in the TM.
The fact that reservoirs stock energy and transformers transform energy is treated as a
known part of the theory / model. Since this information is given in the handouts, this
assumption is not unreasonable. In future the question of when the student pays
attention to this external information, and when the student ignores or misunderstands it
may become more important.
Strictly speaking property matching should be implemented directly in terms of
properties in the class structure, e.g. I could declare as part of the reservoir class that
reservoirs stock energy, and as part of the EF-battery class in the EF, that EF-batteries
stock energy. However the natural way to do this in CLIPS would be to make "stocks" a
slot and "energy" its value. This is not possible in CLIPS because CLIPS rules must
always match on a specific named slot and it is not possible to write a general rule that
matches on any slot. So property matching declarations are implemented as part of the
fact base, although conceptually they are part of the object structure.
So, in the battery-bulb experiment for the ideal student, property matching would create
a reservoir for the battery and a transformer for the bulb.
Recognising Energy Transfers

In order to build the energy chain, the student must identify the energy as a relation
between two other entities (a source and a destination) in order to create it as an arrow;
and the student must focus on the energy process as an abstraction in itself.
A sequence of rules transforms one form of energy information to another. The two key
points are: that each form of energy is derived in a sequence; and that energy transfers
(in the theory model) should be created from energy processes, not from any earlier
stage. So the main sequence is
observational property (etc) -> energy property
energy properties -> energy relation
energy relation -> energy process (IA)
energy process (IA) -> transfer (TM)
Misconceptions and "quick fixes" are modeled by skipping phases in this sequence.
During the correction phase, some stages can be skipped to solve specific problems.
The process of reasoning about specific objects creates the energy properties gives and
gets, which has already been described in the section Determine Energy Properties. If
there is an object that gives some kind of energy connected to an object which gets that
same kind of energy, the following rules connect those objects with an energy relation.
rel-electrical-flow
If an object gives electricity
and is in the same circuit as an object that gets electricity
then there is an energy relation for electricity from the first object to the second
rel-work-flow
If an object gives work
and is on the same string as an object that gets work
then there is an energy relation for work from the first object to the second
rel-heat-flow
If an object gives heat and another object gets heat
then there is an energy relation for heat from the first object to the second
rel-light-flow
If an object gives light and another object gets light
then there is an energy relation for light from the first object to the second
In the first experiment, the following energy relations are created:
(energy-relation light EF-bulb-1 EF-environment-1)
(energy-relation electricity EF-battery-1 EF-bulb-1)
(energy-relation heat EF-bulb-1 EF-environment-1)
Creating an intermediate abstraction for energy
process-make-energy-process

This rule takes an energy relation and creates an instance for an energy process in the
intermediate abstractions.
The intermediate energy abstractions for the battery-bulb experiment (ideal model) are:
(object IA-electricity-1 (is-a electricity IA-energy)
(source EF-battery-1)
(destination EF-bulb-1))
(object IA-heat-1 (is-a heat IA-energy)
(source EF-bulb-1)
(destination IA-environment-1))
(object IA-light-1 (is-a electricity IA-energy)
(source EF-bulb-1)
(destination IA-environment-1))
When an intermediate abstraction exists for an energy process, then rule
OP-direct-equivalence
asserts the goal to create an instance of a transfer for that energy process.
Create the energy chain entity that corresponds to the Experimental Field or Abstract
entity
A single operator creates all the elements in the energy chain. This operator takes a goal
of the form
(make-labelled-tm-instance <TM-class> <class> <entity>)
that is, to make an instance of a reservoir, transformer or transfer as specified to
correspond to a particular entity in s aprticualr class of the experimental field or
intermediate abstractions. This operator is implemented by rules which create the
instance in the theory / model and associate it with the appropriate entities:
OP-make-labelled-tm-instance-1
OP-make-labelled-tm-instance-2
If transfers are created from energy processes then a housekeeping rule
OP-whats-transferred
stores the mode of each energy transfer.
Incorporate the new entity into the energy chain
In the ideal model, transfers are created only after the reservoirs and/or transformers at
either end. So interconnections are made as transfers are created. These are made by
rules
OP-link-transfer-from-TM-EF
OP-link-transfer-to-TM-EF

These rules add the transfer to the inputs and outputs slots of the chain nodes at eaither
and they put the chain nodes in the transfers-from and transfers-to slots of the transfer.
Other rules deal with the non-ideal casewhere the transfer has been created before its
end-points are known. When the source or destination of a transfer becomes known,
these rules maintain the correct connections:
OP-link-transfer-from-TM
OP-link-transfer-to-TM
OP-link-source-to-transfer-TM
OP-link-dest-to-transfer-TM
Label reservoirs, transfomers and transfers
These rules give a value to the slot written-label in an element in the energy chain:
OP-label-transfer-by-mode
OP-label-transfer-by-medium
OP-label-chain-node
There are two rule for labelling transfers. The first rule is used when the students have
created intermediate abstractions for energy processes. It looks up the energy mode for
that type of energy process and assigns that to written-label. The second rule
operates when the problem-solver has associated an energy transfer with a physical
object in the experimental field instead of an abstraction (Student Models 2 and 3 only).
It assigns the name of that object to the written-label. The third rule labels
reservoirs and transformers: it assigns the name of the physical or abstract object to that
reservoir or transformer.
Continue....
These forward reasoning processes are repeated until no more rules can fire. At this
point the chain is printed out and then the checking procedure is initiated.
Tracing the energy chain
These rules demonstrate that the energy chain (as a collection of instances) is separate
from the process of building the energy chain (the rules and facts). When the energy
chain has been built, these rules print it out.
An energy chain is a directed graph, in which chain nodes (reservoirs and transfers) are
nodes and transfers are directed links. These rules give a depth-first walk along the
energy chain.

When the energy chain is complete (or the processing is complete), then the energy
chain is printed out. This is done by the following rules:
trace-start
trace-start-of-circular-chain-1, -2
trace-a-reservoir
trace-a-transformer
trace-a-transfer
trace-a-chain-nodes-outputs
trace-a-chain-nodes-inputs
traced-this-item
trace-node-along-an-output
trace-along-a-transfer
trace-tidy-1, -2, -3, -4
The trace starts at a reservoir (or rather, at any chain node with no inputs). For each
reservoir or transformer, it prints the label of the node, then lists its inputs and outputs
(inputs first) Then it steps along the outputs. Each output is a transfer, so the transfer is
traced. The effect is a depth-first walk along the energy chain, from start to end. The
trace is prevented from looping and it is prevented from repeating information about a
node (e.g. even if both heat and light are transferred to the environment, information
about the environment is only stated once).
The control in the trace is rather fussy and depends on "control facts" and on rule
salience. trace-chain control facts state what is to be traced next and traced-chain
control facts note what has already been traced.
The trace for the battery-bulb experiment ideal model is as follows:
Chain start:
A reservoir labelled battery...
.... which gives out electrical-work
A transfer labelled electrical-work goes from battery to bulb
A transformer labelled bulb...
.... which takes in electrical-work
.... which gives out radiation
.... which gives out heat
A transfer labelled radiation goes from bulb to environment
A transfer labelled heat goes from bulb to environment
A reservoir labelled environment...
.... which takes in radiation
.... which takes in heat
The Checking Phase

Checking in ModelCHENE operates against specific evaluation criteria represented by
rules. There are two kinds of checking rules. One kind of rule checks that the energy
chain corresponds to the specification in the theory model. The second kind checks that
the experimental field has been properly understood. Each checking rule that operates
asserts that something is in need of correction, and notes what it is. The checking rules
are independent of the particular chain that is being built - they do not check the answer
against a "correct" solution, but against general evaluation criteria for all chains.
The rules for checking the form of the energy chain are:
valid-bad-chain-start-transformer
valid-bad-chain-end-transformer
valid-isolated-chain-object
valid-bad-chain-start-transfer
valid-bad-chain-end-transfer
valid-isolated-transfer
These rules check for chains that start or end with transfers; for chains that start or end
with transformers, and for isolated reservoirs, transformers or transfers that are not part
of a chain. These rules are not complete - other rules might be added, e.g. for detecting
circularities or for detecting chains that are not fully connected.
The rules for checking that the experimental field are:
valid-missing-output-energy
valid-missing-input-energy
valid-energy-going-nowhere
valid-energy-from-nowhere
These rules check for objects that give energy for which there is no corresponding get
(and vice versa) and also for energy processes that lack a source or a destination. These
rules are not complete, although they deal with all the situations that arise so far. There
are no rules at present for dealing with missing energy relations or for properties that
are not turned into energy properties.
Finally, there are two rules which end the validation phase.
validation-end-good
validation-end-bad
The first rule comments on correct solution, i.e. one that passes all the evaluation
criteria, both in the energy chain and in the experimental field. The second rule deals
with incorrect solutions by shifting control into the correction phase.
For the ideal model, the checking phase reports no errors and no corrections are
performed.

The Correction Phase
There are fewer correction rules than there are validation rules. There are two reasons
for this. First, the students may not know how to correct some errors. Second, a single
underlying error can cause a sequence of checking problems. If the problem-solver (or
student!) fixes one error and then returns to the forward reasoning phase, this can cause
several checking problems to be corrected at once.
The correction rules are:
correct-bad-chain-start-transfer
correct-bad-chain-end-transfer
correct-missing-output-energy
correct-missing-input-energy
The first two rules deal with the problem of a transfer that goes (or comes from)
nowhere. They create a reservoir and send the transfer to it (or take it from that
reservoir). However they do not link the new reservoir to anything in the experimental
field, so they do not fix whatever underlying misconception the student has about the
experimental field that has led to the problem.
The second two rules create an energy process from a single gives (or a single gets) in
the experimental field. These rules enable students to create an energy process without
knowing where it goes to (or comes from), as in heat or light for the first experiment.
The forward reasoning phase will then turn this process into a transfer, although it will
be a transfer with no destination (or source).
These rules are not used by the ideal model.
Discussion of Reasoning
There are both commonalities and differences in the chains that different students
create for a single experiment. For example, in the first experiment all of the students
eventually identified the battery as a reservoir and the bulb as a transformer, but while
some students identified heat and light with transfers, others did not. Similarly, protocol
analysis has also identified both commonalities in the way that the chains are created
and variations between individuals. Analysis of the students' energy chains and of the
protocols has shown that in general some aspects of the task cause difficulties for most
students, and other aspects are more difficult for some students than for others.

It is important to distinguish between the understanding (and modelling) of general
cognitive processes and the understanding of differences between individuals. We
therefore have two problems:
- to identify and model general problem-solving processes
- to understand and model how variations can arise.
In our approach, we concentrate on general mechanisms which might underly problem-
solving behaviour and we look for small perturbations in those processes which can
account for variations in that process.
We need to model the idea that although all the students may have one overall common
goal, that of building an energy chain, they use very different operations to build the
chain (with more or less success).
The model of reaoning that we have chosen uses:
- specific rules - to model detailed inferences that apply only to one piece of knowledge
- general operators - to model general processes that can be applied to many pieces of
knowledge
- goals - which control the focus of reasoning
- phases of problem-solving - which distinguish between different kinds of reasoning
Our model is similar in some respects to a GOMS model (John et al 1990). We use the
idea of goals to be fulfilled and operators that can fulfill those goals.
One alternative architecture would be to build in fixed reasoning strategies and
explicitly change the strategy or details of the strategy to model different students. This
gives an accurate model for each student we study but it is descriptive rather than
predictive and it does not generalise easily to cover more students. At the opposite
extreme, we could go to a low-level theory of memory and learning such as ACT*
(Andersonet al 1984) and hope that this explains the students' problem-solving. The
problem here is that it may be hard to map such a low-level theory onto the observed
events.
9. Modeling Different Problem-Solving Processes
ModelCHENE can model different problem-solvers. These can vary in
* intermediate abstractions that are available;
* intermediate abstractions that can be created by rules and operators;
* application or not of linear causal reasoning;
* which correction rules are available.

The role of intermediate abstractions is discussed in the following section.
For the battery-bulb experiment only, ModelCHENE 3b can model four different
problem-solvers which produce four different energy chains.
The switch for the different models is set by one of four different facts.
(model ideal)
(model standard-student-1)
(model student-2)
(model student-3)
The user is asked which model is wanted and these facts are set up and maintained by
rules
util-get-problem-solver-model-1
util-get-problem-solver-model-2
util-get-problem-solver-translate
util-get-student-model-tidy
and the starting facts problem-solver-model-facts.
The system starts up with the Student Model 1 set up. If the user chooses a different
problem-solving model for the battery-bulb experiment; then the system alters the
knowledge available, adding or removing some. The differences between the models
are:
The ideal model has four rules which are capable of creating the environment as a
destination for heat and light:
env-make-environment-for-heat
env-make-environment-for-light
env-heat-to-environment
env-light-to-environment
Student Models 2 and 3 both lack the rules which create energy processes
process-make-energy-process
correct-missing-energy-output
correct-missing-energy-input
They lack the factual knowledge that energy process map onto transfers and they have
extra incorrect knowledge that wires transmit energy and that energy transfers represent
things that transmit energy.
Student Model 2 has a rule which allows several transfers to represent the same energy
relation.
model-identical-transfers
Student Model 3 has two rules to make transfers for electricity:
model-circuit-relations which makes electrical energy relations two-way
model-single-transfers which creates a transfer for each relation.

The choice of linear causal resoning or not is set by the rule
model-set-flat-control
which if fired switches out all the linear control rules (default) and switches in flat-
control-start and flat-control.
Rules are made available or not to the problem-solver by including a unique pattern in
the LHS of each rule. If a fact exists for this pattern then the rule is available; if no such
fact exists then therule is not available. These patterns are of the form
(knows student <rule-name>)
10. Intermediate Abstractions
ModelCHENE's problem-solving relies on the use of intermediate abstractions. These
are elements that are neither physical objects in the experiment nor elements of the
theory / model. The "environment" is an intermediate abstraction which the expert is
able to use but the students (at least during the first experiment) are not. Students who
asked themselves "where do heat and light go" were not ableto create such an
abstraction but chose physical objects (eyes and body!) instead.
Similarly the idea of energy undergoes a transformation during problem-solving. It
starts as an observable property of a physical object (the bulb is hot); moves on to an
energy property of a physical object (the bulb gives light); then to an energy relation
between two physical objects (the bulb gives light to the envionment); and finally to an
energy process in which the focus is on the energy itself rather than on the physical
objects. Energy processes are also intermediate abstractions, and it is from energy
processes that energy transfers are created.
We have explored what happens when these abstractions are removed. Figure 4 shows
that the ideal model is aware of one abstraction, the environment, that the other student
models do not use. Simply removing the environment from the initial knowledge
creates an energy chain that is similar to the "ideal" but with two final reservoirs, one to
accept heat and one to accept light, neither of which are given a label (Figure 5, Student
model 1). These two reservoirs are created because the checking phase states that the
energy chain cannot end with two transfers going nowhere - it must end with a
reservoir. The correction phase creates a reservoir to accept each transfer. But there is
nothing in the experimental field with which to connect the reservoirs so they are not
labelled.

Figure 4. Input models of the experimental field for the battery-bulb
experiment
Battery Bulb
Hot
Shining
Battery Bulb
Wire
Wire
(a) All Student Models
Battery Bulb
Hot
Shining
Battery Bulb
Wire
Wire
(b) "ideal" Model Only
Environment
We have also explored what happens when the problem-solver is unable to create
energy processes. We have removed the rules that create energy processes and replaced
them with knowledge that over-generalises the rules for reservoirs and transformers
(Figure 5, Student Models 2 and 3). That is, we have added some new knowledge that
wires transmit energy and that transfers represent , not a mode of energy transfer, but a
physical transmissions medium. The effect is that, just as reservoirs are created by
property matching on stores, so transfers are created by property matching on
transmits. Energy chains are created in which the battery and bulb are represented as
before, but two transfers are created between them. The transfers are each labelled with
the word "wire". (The use of "electrokinetic" knowledge determines the direction of the
arrows in Models 2 and 3.) There is no transmissions medium for heat or light, so they
are not represented as transfers at all (although their presence is noted by the problem
solver in the former of gives properties of the bulb).

11. R e s u l t s
Our problem-solver is able to model significant aspects of the problem-solving
behaviour for the first experiment.
Figure 5. Model energy chains derived for the battery-bulb experiment
Battery
Reservoir
Bulb
Transformer
Electrical
Work
Heat
Radiation
Environment
Reservoir
Reservoir
Battery
Reservoir
Bulb
Transformer
Electrical
Work
Heat
Radiation
Reservoir
Student Model 1
Battery
Reservoir
Bulb
Transformer
Battery
Reservoir
Bulb
Transformer
Wire
Student Model 2 Student Model 3
"Ideal" Model
Wire
Wire
Wire
Figure 5 shows the energy chains produced by the problem-solver for the battery-bulb
experiment. We compare the energy chains produced by the models with the chains
produced by the expert (Figure 1) and by the student pairs (Figure 2). We compare in
terms of the form of the energy chains and the way they have been labelled. In general
the forms of the chains corrrespond closely to the students' chains, but the labelling is
different, especially for transfers.
The different forms of the chains produced by the Student Models correspond closely to
those produced by the actual students. The "ideal" model corresponds closely to the
expert's chain; Student Model 1 to Daniel/Susan; Student Model 2 to Fiona/Lawrence;
and Student Model 3 to Penny/Frederic. In all the model chains, as in all the real chains,
the battery is correctly identified as the first reservoir and the bulb is correctly identified
as a transformer. The order of elements in the chains is the same.

The three different concepts of electricity transfer are shown in the three student models
- a single abstract transfer, two transfers from the battery to the bulb and a "circuit".
Student Model 1 and the "ideal" model both create a single transfer for electrical work.
This represents an abstract transfer of energy. Student Models 2 and 3 and they each
create two transfers for electricity. Both of these features correspond to features of the
chains produced by Fiona-Lawrence and Penny-Frederic. Student Models 2 and 3 have,
like the students, relied on mapping a physical transmission medium in the
experimental field (i.e. the wires) to a transfer in the theory/model.
The "ideal" student model and Student Model 1 create energy transfers transfers for
heat and light. Student Models 2 and 3 do not create energy transfers for heat and light.
This again relates to the need to find a physical object to represent as a transfer. Heat
and light do not have an obvious transmissions medium, and so they are not represented
at all in these energy chains.
The final reservoir for the environment appears only in the problem-solver's "ideal"
chain, as it does in the experts' chain. The environment does not appear in any of the
model student chains, just as it does not appear in the real students' chains. Like Peggy-
Fabien, Student Model 1 treats heat and light as transfers that must "go somewhere".
And like Daniel-Susan' Student Model 1 created two boxes to take this light and heat.
However, Student Model 1 considers the boxes to be reservoirs whereas Daniel-Susan'
considered that heat and light were transformed into something else and therefore their
destinations were transfomers. Student Model 1 is simply driven by the model
constraint that a chain must end with a reservoir, and therefore the model destinations
are reservoirs instead. Student Models 2 and 3 do not include transfers for light and
heat at all, and they do not include the environment. This very similar to Penny-Frederic
and Fiona-Lawrence.
We did not attempt to model the idiosyncratic choice of "eyes" and "body" as labels for
the final reservoirs in Daniel-Susan's chain. Student Model 1 leaves those reservoirs
unlabelled.
The greatest differences are found in the labelling of transfers. All transfers in Student
Model 1 and the "ideal" model are labelled with a mode of energy, as given in the
problem specification. The expert did indeed use these labels, but in fact no students
used them. Instead they used a variety of informal names, sometimes referring to
intermediate abstractions such as electrical current or "arrival of energy". The labelling
of the transfers for student models 2 and 3 have used the "wires" as a label. This is

similar to Fiona-Lawrence, but differs from Penny-Frederic who used "arrival of
energy" as a label instead. Student Model 3 does not fully represent Fiona-Lawrence's
solution. They labelled one of their transfers "wires-" and the other"heat", whereas we
label both "wire". However the dialogue suggests that they did indeed consider the
wires as transfers, and added heat as an afterthought. Clearly there is room for further
exploration of why different students choose particular labels for transfers.
Figure 7 shows the energy chains ModelCHENE produces for the second and third
experiments. These correspond exactly to the experts' energy chains for these
experiments (Figure 1)
Figure 7. Model energy chains for the second and third experiments
Model Energy Chain for Experiment 3 - Rising Weight
Battery
Reservoir
Motor
Transformer
Electrical
Work
Reservoir
Weight
Mechanical
Work
Model Energy Chain for Experiment 2 - Falling Weight
Transformer
Electrical
Work
Heat
Radiation
Environment
Reservoir
Reservoir
Weight
Transformer
Motor Bulb
Mechanical
Work
The energy chains are created from the informal abstractions that the students have
made about the experimental field. Some aspects of these informal abstractions are
shown in Figure 6. The abstractions made by the Expert Model correspond to those
needed for the expert's "ideal" energy chain. The three student models produce
abstractions which differ from the experts in ways that affect the energy chain that is
produced. Two important differences are in the informal abstractions of electricity and
heat/light.

Figure 6. Intermediate abstractions derived from the battery-bulb
experiment
Battery Bulb
Battery Bulb
Electricity
Heat
Light
"ideal" Model
Battery Bulb
Battery Bulb
Wire
Wire
Electricity
Electricity
Heat
Light
Battery Bulb
Battery Bulb
Wire
Wire
Electricity
Electricity
Heat
Light
Student Model 2 Student Model 3
Battery Bulb
Battery Bulb
Electricity
Heat
Light
Student Model 1
Environment
Like the Expert Model, Student Model 1 has an informal abstraction of electrical energy
flowing from the battery to the bulb, an abstraction that is not associated with the wires.
Student Model 2 also has an abstraction of electrical energy flowing from the battery to
the bulb, but in this case it is associated with the idea of energy flowing through the
medium of the two wires. Student Model 3's abstraction of electricity is associated with
the idea of the wires as a medium and also with the electrokinetic idea of electrons
moving round a circuit.
Heat and light are abstracted to different extents in the different models. The Expert
Model has an abstract notion that they flow from a source to an (abstract) destination.
The three student models see heat and light as coming from some source but they do not
go anywhere, so they are unable to create energy processes for them.

12. Architecural Issues: The Use of CLIPS
In this section, we consider the advantages and limitations of CLIPS as an
implementation architecture for a problem-solver in a learning domain.
CLIPS has proved to be a useful tool for modelling many aspects of problem-solving.
The CLIPS object structure supports the division of knowledge into different domains
and the formation of relations between items in those domains. The CLIPS rules are
capable of modelling the different tasks involved in building the energy chain, the
specialised reasoning about objects and to a limited extent they are capable of
modelling general-purpose operators. However, CLIPS has two important limitations
which raise the question of whether CLIPS is an ideal tool for use in later research to
model learning and meta-reasoning during problem-solving.
The CLIPS object structure is static. The sytem builder specifies the class structure with
the appropriate slots in each class. New instances of objects in that structure are created
at run-time, and will inherit the appropriate slots. CLIPS can modify the values in slots.
However, when an instance is created it is "fixed" as a member of some classes and not
of others, as having some slots but not others. A CLIPS system cannot create new
classes at run-time, nor change the classes to which an instance belongs, nor add new
slots to an object or a class.
CLIPS cannot model the learning of rules. CLIPS does not create new rules nor modify
existing rules while it is running. We could model this very crudely by switching in pre-
written rules during problem-solving.
In sum, CLIPS is well suited to modelling the accretion of knowledge in the form of
knew instances of existing classes, new facts and new relations between classes.
However, it is poorly suited to modelling the tuning or restructuring of existing
knowledge.
CLIPS is also not well suited to the implemetation of meta-reasoning. CLIPS supports
special-case rules and is based purely on data-driven reasoning. It offers some choice
of conflict resolution strategies, but it offers no support for goal-directed reasoning nor
for reasoning about its own structures. In particular, it has proved to be very difficult to
implement general problem-solving operators and operators for focussing control on
particular problem-solving goals when these goals refer to anything in the CLIPS object
structure.

WithinModelCHENE, we want to model general problem-solving operators (Johnet al
1990) . A general operator can be applied to many kinds of object. For example,
ModelCHENE's property matching operator potentially applies to any experimental
field item and any theory model item. This is supported by CLIPS. But more than this -
some general operators should generalise over many slots. The property matching
operator should read:
if there is an EF object that has some slot with a particular value
and there is a TM class that has the same slot with the same value
then create an instance of that TM class and put it into the energy chain.
This single operator should work for all slots that appear in both TM items and EF
items. However CLIPS rules do not allow generalisation over slots. Each CLIPS rule
must specify exactly which slots the rule operates on. So, to implement general
operators using CLIPS rules, some workaround is needed.
Our implementation of operators and goals has used the following workarounds:
(a) make those energy properties which could be used by a general operator into facts
associated with the object (or its class) rather than slots in the object (or inherited from
its class).
So, for example, to satisfy the property matching operator we do not use the slot
declarations:
(defclass reservoir .... (slot stocks (default-value energy)))
(defclass transformer .... (slot transforms (default-value energy)))
Instead we create matchable-properties facts, of the form
(matchable-properties reservoir stocks energy)
(matchable-properties bulb transforms energy)
Then we can make inferences about similar properties of the theory model, e.g.
(matchable-properties bulb-1 transforms energy)
(matchable-properties battery-1 stocks energy)
and our general property matching operator is a single rule that can work appropriately
on all these different facts.
Unfortunately, slots have many other advantages (inheritance, etc) and this workaround
loses all those other advantages of using slots. It cannot therefore be used everywhere.

(b) Implement a single general operator in terms of several rules, each rule of which
deals with a single slot. This is possible but laborious and it is easy for these similar
rules to get out of step with each other.
The goal template structure has operated somewhat in this way. Different property rules
match on each gives and gets. This is satisfactory for these rules since the property rules
are all slightly different in any case.
Ideally we would have liked to extend the use of goals into into more complex goal-
directed reasoning, for example:
if we have a goal of showing that an object is a transformer,
create a sub-goal to show that it transforms energy
which creates two further sub-goals to find out what that object
gives and what it gets
and shows that they are different.
but the difficulty of writing general goals in CLIPS is such that this has not been
implemented.
Implementation
We now list some of the limitations of CLIPS 6.0 which have affected the
implementation. They do not affect the overall design of ModelCHENE nor its potential
capabilities in future.
(1) Instance name matching
Storing instance names in facts or slots is fine, but there is a problem matching on
them. Instances on the LHS of a rule must always be bound to an unistantiated variable.
(This looks like a bug in CLIPS 6.0.) To look up an instance in a slot and then find the
properties of the that instance, this order works:
(defrule trace-a-chain-nodes-outputs
?node <- (object (is-a chain-node).....
(trace-chain ?node)
=>
....)
but this (logically equivalent) order doesn't work:
(trace-chain ?node) ; now ?node is bound
?node <- (object (is-a chain-node).....
=>
....)
Workarounds:

(i) Create a my-instance slot for each object which holds the instance name.
Then the rule can be written:
(trace-chain ?node) ; now ?node is bound
(object (is-a chain-node)
(my-instance ?node).....
=>
....)
This allows rules to be written without worrying about the order.
(ii) Always to use the first order. This makes rules hard to read, because the order
forced by CLIPS doesn't necessarily reflect the intended logical structure of the rule.
(iii) Another workaround is to send a message on the RHS of the rule.
This CLIPS problem means that some rules are more sensitive than they should be to
the order in which things are placed on the LHS. Solution (i) gets round this problem
most neatly.
(2) Bug in CLIPS type checking for instances
The distinction between symbols and instances is not maintained well in CLIPS 6.0.
Some CLIPS operations work for symbols but not for instances. Conversely, some
operations that one would expect to work only for instances work also for symbols.
(CLIPS 6.02 may have fixed this?)
For example, the function (length) can be used on a list of symbols but not on a list of
instances. That when I want to a rule to test whether a multislot that contain a list of
INSTANCES is empty or not. If the multislot is of type SYMBOL, then I can use
(length) on the LHS of the rule to test for an empty slot; and conversely I can
use(length)on the RHS of a rule for an INSTANCE multislot . But if the multislot is
of type INSTANCE, then (length) on the LHS gives a syntax error, for violation of
type restrictions
The workaround is: all slots that hold instance names are declared to be of type
SYMBOL instead of INSTANCE. A slot my-instance is created in each object, which
holds the instance name in SYMBOL format rather than INSTANCE format.
Simply storing an instance in a slot of type SYMBOL has the effect of storing it in
SYMBOL format. There's no need for explicit conversation.
(defrule init-connect-motor-to-bob

?motor <- (object (is-a EF-motor) (string none))
?string <- (object (is-a EF-string)...
=>
(modify-instance ?motor (string ?string)) ...
On the LHS of the rule, the ?string is an INSTANCE. The slot string is of type
SYMBOL, and so the type of ?string is converted automatically to SYMBOL.
The only RHS operations which cannot be applied to a SYMBOL is modify-instance.
For these operations the real instance name, in INSTANCE format, must be obtained
from the LHS of the rule. In the previous example, the term ?motor could not have been
obtained from the my-instance slot. It had to be obtained using <-, which returns an
INSTANCE type, in order to specify the instance for modify-instance.
(3) Multiple rule firing on multi-valued slots
This is a common feature of rule-based systems. When the LHS of a rule refers to a slot
and the RHS of the rule changes that same slot, the rule may fire again, and again, and
again. So any rule whose purpose is to take a multi-valued slot and add another value to
it has to be prevented from firing again and again indefinitely. The workaround for this
is to pick up the values on the RHS of the rule using (bind) instead of using pattern
matching on the LHS.
e.g. using bind on the RHS works:
(defrule init-object-contacts-wire
(phase initialise)
?EF-object <- (object (is-a EF-circuit-object)
(wires $? ?wire&~none $?))
=>
(bind ?contacts (send ?EF-object get-contacts))
(modify-instance ?EF-object
(contacts (insert$ ?contacts 1 ?wire))))
but using pattern matching on the LHS would add ?wire again and again
forever:
(defrule init-object-contacts-wire-buggy
(phase initialise)
?EF-object <- (object (is-a EF-circuit-object)
(wires $? ?wire&~none $?)
(contacts ?contacts))
; using pattern matching on LHS
=>
(modify-instance ?EF-object
(contacts (insert$ ?contacts 1 wire))))

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