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Graphs, Frames and
Related Structures
BY: SURBHI SAROHA
Syllabus
โ€ข Associative networks
โ€ข Frame structures
โ€ข Conceptual dependancies
โ€ข Scripts
Associative networks
โ€ข Neural associative memories (NAM) are neural network models consisting of neuronlike and synapse-like
elements.
โ€ข At any given point in time the state of the neural network is given by the vector of neural activities, it is
called the activity pattern.
โ€ข Neurons update their activity values based on the inputs they receive (over the synapses). In the simplest
neural network models the input-output function of a neuron is the identitiy function or a threshold
operation.
โ€ข Note that in the latter case the neural activity state is binary: active or inactive. Information to be processed
by the neural network is represented by activity patterns (for instance, the representation of a tree can an
activity pattern where active neurons display a tree's picture).
โ€ข Thus, activity patterns are the representations of the elements processed in the network. A representation is
called sparse if the ratio between active and inactive neurons is small.
Contโ€ฆ..
โ€ข Different memory functions are defined by the way how learned patterns can
be selectively accessed by an input pattern.
โ€ข The function of pattern recognition means to classify input patterns in two
classes, familiar patterns and the rest.
โ€ข Pattern association describes the function to associate certain input patterns
with certain memory patterns, i.e., each stored memory consists of a pair of
input and desired output pattern.
Contโ€ฆ.
โ€ข A content-addressable memory is a type of memory that allows for the recall
of data based on the degree of similarity between the input pattern and the
patterns stored in memory.
โ€ข It refers to a memory organization in which the memory is accessed by its
content as opposed to an explicit address like in the traditional computer
memory system.
โ€ข Therefore, this type of memory allows the recall of information based on
partial knowledge of its contents.
Frame structures
โ€ข Frames are an artificial intelligence data structure used to divide knowledge into
substructures by representing "stereotyped situations". They were proposed
by Marvin Minsky in his 1974 article "A Framework for Representing Knowledge".
โ€ข Frames are the primary data structure used in artificial intelligence frame language;
they are stored as ontologies of sets.
โ€ข Frames are also an extensive part of knowledge representation and
reasoning schemes.
โ€ข They were originally derived from semantic networks and are therefore part of
structure based knowledge representations.
Frame Network - Example
Description of Frames
โ€ข Each frame represent either a class or an instance.
โ€ข Class frame represents a general concept whereas instance frame represents a
specific occurrence of the class instance.
โ€ข Class frame generally have default values which can be redefined at lower levels.
โ€ข If class frame has actual value facet then decedent frames can not modify that value.
โ€ข Value remains unchanged for subclasses and instances.
Inheritance in Frames
โ€ข Suppose we want to know nationality or phone of an instance-frame13.
โ€ข These information are not given in this frame.
โ€ข Search will start from frame13 in upward direction till we get our answer or have
reached root frame.
โ€ข The frame can be easily represented in prolog by choosing predicate name as frame
with two arguments.
โ€ข First argument is the name of the frame and second argument is a list of slot - facet
pair.
Features of Frame Representations
โ€ข Frames can support values more naturally than semantic nets (e.g. the value
25)
โ€ข Frames can be easily implemented using object-oriented programming
techniques.
โ€ข Demons allow for arbitrary functions to be embedded in a representation.
โ€ข But a price is paid in terms of efficiency, generality, and modularity !
โ€ข Inheritance can be easily controlled.
Conceptual dependancies
โ€ข Conceptual dependency theory is a model of natural language understanding used
in artificial intelligence systems.
โ€ข Roger Schank at Stanford University introduced the model in 1969, in the early days of
artificial intelligence. This model was extensively used by Schank's students at Yale
University such as Robert Wilensky, Wendy Lehnert, and Janet Kolodner.
โ€ข Schank developed the model to represent knowledge for natural language input into
computers. Partly influenced by the work of Sydney Lamb, his goal was to make the
meaning independent of the words used in the input, i.e. two sentences identical in meaning,
would have a single representation. The system was also intended to draw logical inferences.
โ€ข The model uses the following basic representational tokens:
Contโ€ฆ.
โ€ข real world objects, each with some attributes.
โ€ข real world actions, each with attributes
โ€ข times
โ€ข locations
โ€ข A set of conceptual transitions then act on this representation, e.g. an ATRANS is used to
represent a transfer such as "give" or "take" while a PTRANS is used to act on locations
such as "move" or "go". An MTRANS represents mental acts such as "tell", etc.
โ€ข A sentence such as "John gave a book to Mary" is then represented as the action of an
ATRANS on two real world objects John and Mary.
Scripts
โ€ข Script theory is a psychological theory which posits that human behaviour largely
falls into patterns called "scripts" because they function analogously to the way a
written script does, by providing a program for action.
โ€ข Silvan Tomkins created script theory as a further development of his affect theory,
which regards human beings' emotional responses to stimuli as falling into
categories called "affects": he noticed that the purely biological response of affect
may be followed by awareness and by what we cognitively do in terms of acting on
that affect so that more was needed to produce a complete explanation of what he
called "human being theory".
Contโ€ฆ..
โ€ข In script theory, the basic unit of analysis is called a "scene", defined as a
sequence of events linked by the affects triggered during the experience of
those events.
โ€ข Tomkins recognized that our affective experiences fall into patterns that we
may group together according to criteria such as the types of persons and
places involved and the degree of intensity of the effect experienced, the
patterns of which constitute scripts that inform our behavior in an effort to
maximize positive affect and to minimize negative affect.
Thank you๏Š

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Graphs, frames and related structures

  • 1. Graphs, Frames and Related Structures BY: SURBHI SAROHA
  • 2. Syllabus โ€ข Associative networks โ€ข Frame structures โ€ข Conceptual dependancies โ€ข Scripts
  • 3. Associative networks โ€ข Neural associative memories (NAM) are neural network models consisting of neuronlike and synapse-like elements. โ€ข At any given point in time the state of the neural network is given by the vector of neural activities, it is called the activity pattern. โ€ข Neurons update their activity values based on the inputs they receive (over the synapses). In the simplest neural network models the input-output function of a neuron is the identitiy function or a threshold operation. โ€ข Note that in the latter case the neural activity state is binary: active or inactive. Information to be processed by the neural network is represented by activity patterns (for instance, the representation of a tree can an activity pattern where active neurons display a tree's picture). โ€ข Thus, activity patterns are the representations of the elements processed in the network. A representation is called sparse if the ratio between active and inactive neurons is small.
  • 4. Contโ€ฆ.. โ€ข Different memory functions are defined by the way how learned patterns can be selectively accessed by an input pattern. โ€ข The function of pattern recognition means to classify input patterns in two classes, familiar patterns and the rest. โ€ข Pattern association describes the function to associate certain input patterns with certain memory patterns, i.e., each stored memory consists of a pair of input and desired output pattern.
  • 5. Contโ€ฆ. โ€ข A content-addressable memory is a type of memory that allows for the recall of data based on the degree of similarity between the input pattern and the patterns stored in memory. โ€ข It refers to a memory organization in which the memory is accessed by its content as opposed to an explicit address like in the traditional computer memory system. โ€ข Therefore, this type of memory allows the recall of information based on partial knowledge of its contents.
  • 6. Frame structures โ€ข Frames are an artificial intelligence data structure used to divide knowledge into substructures by representing "stereotyped situations". They were proposed by Marvin Minsky in his 1974 article "A Framework for Representing Knowledge". โ€ข Frames are the primary data structure used in artificial intelligence frame language; they are stored as ontologies of sets. โ€ข Frames are also an extensive part of knowledge representation and reasoning schemes. โ€ข They were originally derived from semantic networks and are therefore part of structure based knowledge representations.
  • 7. Frame Network - Example
  • 8. Description of Frames โ€ข Each frame represent either a class or an instance. โ€ข Class frame represents a general concept whereas instance frame represents a specific occurrence of the class instance. โ€ข Class frame generally have default values which can be redefined at lower levels. โ€ข If class frame has actual value facet then decedent frames can not modify that value. โ€ข Value remains unchanged for subclasses and instances.
  • 9. Inheritance in Frames โ€ข Suppose we want to know nationality or phone of an instance-frame13. โ€ข These information are not given in this frame. โ€ข Search will start from frame13 in upward direction till we get our answer or have reached root frame. โ€ข The frame can be easily represented in prolog by choosing predicate name as frame with two arguments. โ€ข First argument is the name of the frame and second argument is a list of slot - facet pair.
  • 10. Features of Frame Representations โ€ข Frames can support values more naturally than semantic nets (e.g. the value 25) โ€ข Frames can be easily implemented using object-oriented programming techniques. โ€ข Demons allow for arbitrary functions to be embedded in a representation. โ€ข But a price is paid in terms of efficiency, generality, and modularity ! โ€ข Inheritance can be easily controlled.
  • 11. Conceptual dependancies โ€ข Conceptual dependency theory is a model of natural language understanding used in artificial intelligence systems. โ€ข Roger Schank at Stanford University introduced the model in 1969, in the early days of artificial intelligence. This model was extensively used by Schank's students at Yale University such as Robert Wilensky, Wendy Lehnert, and Janet Kolodner. โ€ข Schank developed the model to represent knowledge for natural language input into computers. Partly influenced by the work of Sydney Lamb, his goal was to make the meaning independent of the words used in the input, i.e. two sentences identical in meaning, would have a single representation. The system was also intended to draw logical inferences. โ€ข The model uses the following basic representational tokens:
  • 12. Contโ€ฆ. โ€ข real world objects, each with some attributes. โ€ข real world actions, each with attributes โ€ข times โ€ข locations โ€ข A set of conceptual transitions then act on this representation, e.g. an ATRANS is used to represent a transfer such as "give" or "take" while a PTRANS is used to act on locations such as "move" or "go". An MTRANS represents mental acts such as "tell", etc. โ€ข A sentence such as "John gave a book to Mary" is then represented as the action of an ATRANS on two real world objects John and Mary.
  • 13. Scripts โ€ข Script theory is a psychological theory which posits that human behaviour largely falls into patterns called "scripts" because they function analogously to the way a written script does, by providing a program for action. โ€ข Silvan Tomkins created script theory as a further development of his affect theory, which regards human beings' emotional responses to stimuli as falling into categories called "affects": he noticed that the purely biological response of affect may be followed by awareness and by what we cognitively do in terms of acting on that affect so that more was needed to produce a complete explanation of what he called "human being theory".
  • 14. Contโ€ฆ.. โ€ข In script theory, the basic unit of analysis is called a "scene", defined as a sequence of events linked by the affects triggered during the experience of those events. โ€ข Tomkins recognized that our affective experiences fall into patterns that we may group together according to criteria such as the types of persons and places involved and the degree of intensity of the effect experienced, the patterns of which constitute scripts that inform our behavior in an effort to maximize positive affect and to minimize negative affect.