Expert Systems: Knowledge Representation and Problem Solving, Lecture notes of Artificial Intelligence

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Strong Method Problem

Solving

(Topic 7)

Course Contents

Again..Selected topics for our course. Covering all of AI is

impossible!

Key topics include:

Introduction to Artificial Intelligence (AI)

Knowledge Representation and Search

Introduction to AI Programming

Problem Solving Using Search

Exhaustive Search Algorithm

Heuristic Search

Techniques and Mechanisms of Search Algorithm

Knowledge Representation Issues and Concepts

Strong Method Problem Solving

Soft Computing and Machine Learning

Introduction to Expert system

 Uses knowledge specific to a problem domain

 With the help of human domain experts

 System emulates the expert’s methodology and

performance

 Tend to be specialist, focusing on narrow set of

problems; theoretical and practical

 Human expert provide the knowledge

augmented theoretical understanding of the problem domain with tricks, shortcuts and heuristics for using the knowledge gained through problem-solving experience

Introduction to Expert system

 Because of their heuristic, knowledge-intensive

nature, expert systems generally:

 Support inspection of reasoning processes - provide

information and explanations of choices and decisions

made by the program

 Allow easy modification in adding and deleting skills

from knowledge base - programs are easily prototyped,

tested and changed. Modifying a rule has no global

syntactic side effect

 Reason heuristically, using imperfect knowledge to

get useful solutions – tricks of the trade and rules of

thumb, shortcuts

Architecture of a typical expert system for a particular problem domain.

Heart of expert system i.e. if..then..rules

Interpreter of KB i.e. perform recognize-act

Dotted line – shell modules Control cycle in PS Indicates same for all systems

Why is the separation of KB and

Inference engine necessary?

1. Makes it possible to represent knowledge

naturally, humanly rather than computer code

2. ES builders can focus on capturing and

organizing problem-solving knowledge rather

than its implementation

3. Allows changes to be done separately
4. Allows same control and interface for variety of

systems except for KB and case-specific data

are emptied for new application

Guidelines to determine whether a problem is appropriate for expert system solution:

1. The need for the solution justifies the cost and effort of building an expert

system-save money, time, life etc.

2. Human expertise is not available in all situations where it is needed-save

time, money in remote sites

3. The problem may be solved using symbolic reasoning.
4. The problem domain is well structured and does not require commonsense

reasoning (which is difficult to automate)

5. The problem may not be solved using traditional computing methods.
6. Cooperative and articulate experts exist – they are willing to share

knowledge

7. The problem is of proper size and scope – not to capture ALL expertise

Who is involved in building ES?

 knowledge engineer

 AI language and representation expert  Select software/hardware tools  Help domain expert articulate necessary knowledge  Implement knowledge in correct/efficient KB  Ignorant of application domain

 Domain expert

 Provide knowledge  Worked in the domain area  Understand its problem-solving techniques  Expert problem solver  Responsible to spell out skills to knowledge engineer

 End user

 Determines major design constraint  Should be happy or else effort is wasted  Makes work quicker? Explanations? Correct information to system? Interface ok?

The role of mental or conceptual models in problem solving.

• conceptual model
  • means knowledge engineer’s evolving conception of domain knowledge
  • determines construction of formal KB

Intermediate Design construct

A small expert system for analysis of automotive

problems-goal driven rule chaining

Rule-based Expert System: Production system

and goal-driven prob solving

• rule-based ES represent prob-solving knowledge as if… then…. Rules
• understood as production system model
• Goal-driven expert system- goal initially placed in working memory
• System matches rules conclusion with goal, select a rule and place premise in working memory

Figure 7.6: The production system after Rule 1 has fired.

Resolve conflict Fire Rule 1

Figure 7.7: The system after Rule 4 has fired. Note the stack-based approach

to goal reduction.

• 1 st^ premise of Rule 1 evaluated, match conclusion Rule 4
• Premises of Rule 4 placed in WM

The following dialogue begins with the computer asking the

user about the goals present in working memory.

• Query user

about subgoals

• If all true,ES

determine car

doesn’t start

becoz of bad

sparkplug

The production system at the start of a consultation for

data-driven reasoning.

• breadth-first search is more common in data- driven resoning
• Compare WM with conditions of each rule
• Once all rules are considered,search starts again at beginning
• Examine rules – is info askable? If no, fail. Move to next rule