Applied Artificial Intelligence Unit 1 Semester 3 MSc IT Part 2 Mumbai University

Applied Artificial Intelligence
Total Number of units: 5
Your Host for the subject : Madhav Mishra

Topics To Be Covered
• Review of AI: History, Foundation
and Applications
• Expert System and Applications:
Phases in Building Expert System,
Expert System Architecture, Expert
System versus Traditional Systems,
Rule based Expert Systems,
Blackboard Systems, Truth
Maintenance System, Application of
Expert Systems, Shells and Tools
PPT BY: MADHAV MISHRA 2

Review of AI:
History
• The History of AI had cycles of success &
failures but it kept on introducing new creative
approaches & Systematically refining the best
ones.
• There was no relation between human
intelligence and machine till early 1950.
• Psychologists further strengthened the idea
that humans and other living creatures can be
considered to be information processing
machines.
• Mathematicians provided tools to manipulate
certain or uncertain logical statements
(e.g. probabilistic statements)
• John McCarthy organised a conference on
machine learning in 1956 and since then the
field was known as artificial Intelligence.

• In 1957, the first version of a new program named as General Purpose
Solver (GPS) was developed and test.
• This program was also developed by Newell & Simon.
• The GPS was capable of solving some extent of problems that required
common sense.
• Since the many programs were developed and McCarthy announced his
new development called as LISt Processing Language (LISP) in 1958.
• AI developers used this language rather adopted as the language.
• Marvin Minsky of MIT demonstrated that computer programs could
solve logical problems when confined to a specific domain.
• Another program, named STUDENT, was developed during late 1960
which could solve algebra story problems.
• Fuzzy sets and logic was developed by L. Zadeh in 1960 that had unique
ability to make decision under uncertain conditions.
• Same time a system named SHRDLU was developed by Terry Winogard
at the MIT, AI Laboratory.(SHRDLU was a program that carried out a
simple dialogue(via teletype))

• Then in 1970 came advent of expert systems, these systems where
designed and developed to predict the probability of a solution under
set conditions.
• An expert system is a program that uses logical rules that are derived
from the knowledge of experts to answer the question or solve
problems about a specific domain.
• In 1980, research organisation and corporate sector started
developing AI Systems at faster pace.
• Because of its efficiency the expert systems came into great demand.
• New theories about computer vision was proposed by David Marr,
where it was possible to distinguish an image based on basic
information such as shapes, colour, edge, texture and the shading of
an image.

Foundation Of AI
• Commonly used AI techniques and
theories are rule-based, fuzzy logic,
neural networks, decision theory,
statistics, probability theory, genetic
algorithms, etc.
• Since AI is interdisciplinary in nature,
foundation of AI are in various fields
such as:
1. Mathematics
2. Neuroscience
3. Control Theory
4. Linguistics

• Mathematics:
AI systems use Formal logical methods and Boolean logic (Boole
1847), analysis of limits to what can be computed, probability theory
that forms the basis for most modern approaches to AI, fuzzy logic, etc.
• Neuroscience:
The Science of medicine helps in studying the functioning of brains.
In early studies, injured and abnormal people used to understand that
which exact part of brain is working. Now recent studies use accurate
sensors to correlate brain activity to human thoughts.
By Monitoring individual neurons, monkey can now control a computer
mouse using thoughts alone.
Using Neuroscience researchers are working to know as to how to have
mechanical brain, as such system will require parallel computing,
remapping and interconnections to a large extent.

• Control Theory:
Machines can modify their behaviour in response to the environment
(sense actions).
Steam engine, thermostats, water flow regulator are few examples of
control theory.
In 1950, control theory could only describe linear systems, thus AI
largely arose as the shortcoming for this.
• Linguistics:
Speech demonstrates so much of human intelligence.
Analysis of human language reveals thoughts that can take place in
ways possible.
Simply a small kid can create sentences they have never heard before,
as languages and thoughts are believed to be tightly interwined.

Applications Of
AI
9
• AI finds applications in almost all areas of real-life
applications. Broadly speaking, business, engineering,
medicine, education and manufacturing are the main
areas.
• Business: financial strategies, give advice.
• Engineering: check design, offer suggestions to create
new product, expert systems for all engineering
applications.
• Manufacturing: assembling, inspections & maintenance.
• Education: in teaching.
• Fraud Detection.
• Object identification.
• Space shuttle scheduling.
• Information retrieval.
PPT BY: MADHAV MISHRA

Expert System and
Applications
• One of the goals of AI is to understand the concept of intelligence
and develop intelligent computer programs.
• An example od a computer program that exhibits intelligent
behaviour is an EXPERT SYSTEM (ES).
• Expert Systems are meant to solve real-world problems which
require specialized human expertise and provide expert quality
advice, diagnoses and recommendations.
• An ES is basically a software program or system that tries to
perform tasks similar to human experts in a specific domain of the
problem.
• Expert Systems represent may also be referred as Knowledge-
based expert system.
• Here they provide their knowledge and suggest rules on data used
in the system.
• ES may or may not posses learning components, as once they are
fully developed their performance is evaluated by subjecting them
to real world problem solving solution.

Phases in Building Expert
System
The different interdependent
and overlapping phases involved
in building an ES are categorized
as follows:
• Identification Phase
• Conceptualization Phase
• Formalization Phase
• Implementation Phase
• Testing Phase

• Identification Phase:
In this phase, the knowledge engineer determines important features
of the problem with the help of the human domain expert. The
parameters that are determined in this phase include the type and
scope of the problem, the kind of resources required, the goal and
objective of the ES.
• Conceptualization Phase:
In this phase, knowledge engineer and domain expert decides the
concept, relations and control mechanism needed to describe the
problem-solving method. At this stage, the issue of granularity is also
addressed, which refers to the level of details required in the
knowledge.

• Formalization Phase:
This phase involves expressing the key concepts and relations in some
framework supported by ES building tools. Formalized knowledge
consists of data structures, inference rules, control strategies &
languages required for implementation.
• Implementation Phase:
During this phase, formalized knowledge is converted to a computer
program, initially called prototype of the whole system.
• Testing Phase:
This phase involves evaluating the performance and utility of prototype
system and revising the system, if required. The domain expert
evaluates the prototype system and provides feedback, which helps the
knowledge engineer to revise it.

Expert
System
Architecture

The Architecture of an Expert System (ES) consist of the following major components:
• Knowledge Base (KB): repository of special heuristics or rules that direct the use
of knowledge, facts (productions). It contains the knowledge necessary for
understanding, formulating, & problem solving.
• Working Memory(Blackboard): if forward chaining used It describes the current
problem & record intermediate results.
Records Intermediate Hypothesis & Decisions: 1. Plan, 2. Agenda, 3. Solution
• Inference Engine: the deduction system used to infer results from user input &
KB It is the brain of the ES, the control structure(rule interpreter). It provides
methodology for reasoning
• Explanation Subsystem (Justifier): Traces responsibility & explains the ES
behaviour by interactively answering question: Why?, How?, What?, Where?,
When?, Who?
• User Interface: interfaces with user through Natural Language Processing (NLP),
or menus & graphics. Acts as Language Processor for friendly, problem-oriented
communication. PPT BY: MADHAV MISHRA 15

• Shell = Inference Engine + User Interface
The Human Elements in ESs
Expert: Has the special knowledge, judgement, experience and
methods to give advice and solve problems. Provides knowledge about
task performance.
Knowledge Engineer: Usually also the System Builder
Helps the expert(s) structure the problem area by interpreting and
integrating human answers to questions, drawing analogies, posing
counter examples, and bringing to light conceptual difficulties.
The Expert & the knowledge Engineer should Anticipate Users’ needs &
Limitations when designing Expert Systems

• User: Possible Classes of Users can be
A non-expert client seeking direct advice (ES
acts as a Consultant or Advisor)
A student who wants to learn (ES acts as an
Instructor)
An ES builder improving or increasing the
knowledge base(ES acts as a Partner)
An Expert (ES acts as a Colleague or an
Assistant)

Expert
System
versus
Traditional
Systems
• The basic difference between an ES and a traditional system is
that an ES manipulates knowledge whereas a traditional
system manipulates data.
• The distinction between these systems lies in the manner in
which the problem- related expertise is coded into them.
• In traditional application, problem expertise is encoded in
program as well as in the form of data structures.
• On the other hand, in the ES approach, all the problem related
expertise is encoded in data structures only and not in the
programs.
• Traditional computer programs perform task using
conventional decision-making logic, which is often embedded
as a part of the code in the form of a basic algorithm
containing little knowledge.
• Hence, if the knowledge changes the program as to be rebuilt.

• However, in expert systems, small fragments of human experiences are
collected into a knowledge base.
• This is used to as a reason through a problem.
• A different problem, within the domain of the knowledge base, can be
solved using the same program without having to reprogram the system.
• Another advantage of expert systems over traditional system is that they
allow the use of confidences or certainty factors.
• This is similar to human reasoning where one cannot always conclude
things with 100 % confidence.
• For example, consider the statement IF weather is humid, THEN it might
probably rain.
• The use of words such as if, then, might, probably etc, indicate that there is
some uncertainty involved in the statement.
• Therefor this system using confidence level say 0.9 confidence that it will
rain(similar to concept like probability)

Characteristics Of Expert Systems
Some Key Characteristics that every ES must possess are as follows:
 Expertise : An ES should exhibit expert performance, have high level of skill and possess adequate
robustness. The high-level and skill of an ES aids in problem solving and makes the system cost
effective.
 Symbolic Reasoning: Knowledge in an ES is represented symbolically which can be easily reformulated
and reasoned.
 Self knowledge: A system should be able to explain and examine its own reasoning.
 Learning Capability: A system should learn from it mistakes and mature as it grows. Flexibility provided
by the ES helps it grow incrementally.
 Ability to Provide Training: Every ES should be capable of providing training by explaining the reasoning
process behind solving a particular problem using relevant knowledge.
 Predictive Modelling Power: This is one of the important features of ES. The system can act as an
information processing model of problem solving. It can explain how new situation led to the change,
which helps users to evaluate the effect of new facts and understand their relationship to the solution.

Advantages:
- Helps in preserving scarce expertise.
- Provides consistent answers for repetitive decision, processes and tasks.
- Fastens the pace of human professional or semi-professional work.
- Holds and maintains significant levels of information.
- Provides improved quality of decision making.
- Leads to major internal cost savings within companies.

Disadvantages:
-Unable to make creative responses as human experts would in unusual
circumstances.
-Lacks common sense needed in some decision making.
-May cause errors in the knowledge base and lead to wrong decisions.
-Cannot adapt to changing environments, unless knowledge base in
changed.

Rule Based Expert Systems

Blackboard Systems: (1970)
• A traditional way of combining diverse software modules is to connect
them according to their data-flow requirements.
• The connections are predetermined and direct.
• This approach works well when the modules and the appropriate
communications among modules are static.
• In dynamic environment, the modules and the ordering are subject to
change and cannot be determined until specific data values are known at
the time of execution.
• In such situations, indirect and anonymous communication approach
among modules with the help of intermediary such as blackboard data
repository proves to be extremely useful.
• In this approach all processing paths are possible, and separate moderator
mechanism dynamically selects a path among the possible paths.
• The Information placed on blackboard is public and is made available to all
modules.

A blackboard system consists of three components :
• Blackboard ( BB ),
• Knowledge sources ( KSs), and
• Control unit.
Blackboard
• It is the part of the system that is used for storage of knowledge accessible to all the KSs.
• It is a global data structure used to organize the problem-solving data and to handle
communications between the KSs.
• The objects that are placed on the BB could be input data, partial results, hypotheses,
alternatives and the final solution.
• Interaction among the KS’s is carried out via the BB.
• A blackboard may be partitioned into an unlimited number of sub-blackboards, also
called planes or panels.
• That is, a BB can be divided into several BB levels corresponding to different aspects of
the solution process.
• Hence, the objects can be organized hierarchically into different levels of analysis.

• An object may be stored as a list of attribute values.
• An event that specify the occurrence of a certain situation is created or modified
on the BB.
• It is used to determine which KSs can take part in the problem-solving process at
any given moment.
• An event created on the BB may trigger a number of KSs.
• Each entry to the BB can have an associated certainty factor.
• This is one way the system handles uncertainty in the knowledge.
• The mechanism of blackboard ensures that there is a uniform interface between
each KS and the partial solutions found so far.
• Hence, KSs are fairly independent of each other.

Knowledge Sources
• Knowledge sources are self-selecting modules of domain knowledge.
• Each knowledge source can be viewed as an independent program specialized in
processing a certain type of information or knowledge of a narrower domain.
• Each knowledge source should have the ability to assess itself on whether it should
contribute to the problem solving process at any instance.
• The knowledge sources in a blackboard system are separated and independent.
• Each has its own set of working procedures or rules and each has its own private data
structure.
• It contains information necessary for a correct run of the knowledge source.
• The action part of a knowledge source performs the actual problem solving and
produces changes to the BB.
• It can allow for different kinds of knowledge representation and different inference
mechanisms.
• Hence, the action part of a KS can be a production rule system with forward/backward
chaining or it can be a frame-based system with slot-filling procedures attached to some
slot.
45

Control Unit
• The control components of a blackboard system helps in making runtime decisions regarding the
course of problem solving and the expenditure of problem-solving resources.
• Control unit is separate from individual KS’s.
• In a blackboard system, a separate control mechanism sometimes called as control shell, directs
the problem- solving process by allowing KS’s to respond automatically to the changes made in
blackboard database.
• On the basis of the state of the blackboard and the set of triggered KSs, the control mechanism
chooses a course of action.
• A blackboard system uses an incremental reasoning style: the solution to the problem is built one
step at a time. At each step, the system can:
 execute any triggered KS.
 choose a different focus of attention, on the basis of the state of the solution.
• Under a typical control approach, the currently executing KS activation generates events as it
makes contributions to the blackboard.
• These events are maintained (and possibly ranked) until the executing KS activation is completed.
• At that point, the control components use the events to trigger and activate KS’s.
• The KS activations are ranked, and the most appropriate KS activation is selected for execution.
• This cycle continues until the problem is solved.
46

Truth Maintenance Systems
• Truth maintenance systems (TMSs) were introduced more than ten years ago,
but recently there is an explosion of interest in them and their possible
applications in different areas. In this paper we discuss truth maintenance
from three perspectives:
• Truth maintenance as a data base management facility, which was in fact the
original intention of the TMS.
• Truth maintenance as an inference facility, which provides a way to extend
the role of the TMS in solving problems.
• Truth maintenance as a verification facility, which illustrates a new and
promising application of TMSs in the area of expert systems design.

A TMS is intended to satisfy a number of goals:
• Provide justifications for conclusions
• Recognize inconsistencies
• Support default reasoning
• Remember derivations computed previously

Truth Maintenance Systems can have different characteristics:
• Justification-Based Truth Maintenance System (JTMS)
It is a simple TMS where one can examine the consequences of the current set of
assumptions. The meaning of sentences is not known.
• Assumption-Based Truth Maintenance System (ATMS)
It allows to maintain and reason with a number of simultaneous, possibly
incompatible, current sets of assumption. Otherwise it is similar to JTMS, i.e. it
does not recognise the meaning of sentences.
• Logical-Based Truth Maintenance System (LTMS)
Like JTMS in that it reasons with only one set of current assumptions at a time.
More powerful than JTMS in that it recognises the propositional semantics of
sentences, i.e. understands the relations between p and ~p, p and q and p&q, and
so on.
We will not discuss further LTMSs.

Applications of Expert System

Shells and Tools
Tools - They reduce the effort and cost involved in developing an expert system to large extent.
• Powerful editors and debugging tools with multi-windows.
• They provide rapid prototyping
• Have Inbuilt definitions of model, knowledge representation, and inference design.
Shells - A shell is nothing but an expert system without knowledge base. A shell provides the developers
with knowledge acquisition, inference engine, user interface, and explanation facility. For example, few
shells are given below :
• Java Expert System Shell (JESS) that provides fully developed Java API for creating an expert system.
• Vidwan, a shell developed at the National Centre for Software Technology, Mumbai in 1993. It enables
knowledge encoding in the form of IF-THEN rules.

List of Shells and Tools
List of popular Shells & Tools, which will add to the reader’s
understanding & knowledge of expert systems anf their applications:
• ACQUIRE
• ART(Automated Reasoning Tool)
• CLIPS(C Language Integrated Production Systems)
• FLEX(hybrid ES)
• KNOWLEDGE-CRAFT (ES development toolkit for scheduling, design
& configuration applications)
• K-VISION (Knowledge acquisition and visualization tool, runs on
windows, DOS & Unix)
• MAILBOT (Email agent that reads the email & creates a reply for the
email. Provides filtering, forwarding, notification etc..)

End Of Unit 1

Applied Artificial Intelligence Unit 1 Semester 3 MSc IT Part 2 Mumbai University

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