Download presentation

1
**Artificial Intelligence CAP492**

Dr. Souham Meshoul Information Technology Department CCIS – King Saud University Riyadh, Saudi Arabia

2
**Artificial Intelligence CAP492**

Expert Systems Chapter 8

3
**Connectionist Approach**

Introduction Two main approaches for problem solving using knowledge: Symbolic Approach Expert systems: Feed the system with knowledge. There is no learning !! Connectionist Approach Neural Networks: The system learns from examples by itself.

4
Introduction Procedural/Object Oriented Programming vs Declarative Programming PP/OOP: a program instructs the computer -what to do – how to do – in what oreder Whereas DP: a program describes what to do without a detailed plan of how to do

5
Symbolic Approach

6
**Introduction Expert system**

Expert system: knowledge-based systems or rule based-system. Expert system: a rule based program which encapsulates knowledge from some domain, normally obtained from a human expert in that domain An expert system simulates a human expert in his/her field of expertise in an attempt to solve a particular problem. Expert Systems Do Not Replace Experts, But They Make their Knowledge and Experience More Widely Available Permit Nonexperts to Work Better ES acts as a Consultant or Advisor

7
**Structure of an Expert System**

Major Components Knowledge base (KB): repository of rules, facts (productions) working memory: (if forward chaining used) inference engine: the deduction system used to infer results from user input and KB user interface: interfaces with user external control + monitoring: access external databases, control,... User Interface Inference Engine Knowledge Base

8
**Structure of an Expert System**

Knowledge base The knowledge base contains the knowledge necessary for understanding, formulating, and solving problems Two Basic Knowledge Base Elements Facts Special heuristics, or rules that direct the use of knowledge Knowledge is the primary raw material of ES Incorporated knowledge representation User Interface Inference Engine Knowledge Base

9
**Structure of an Expert System**

Inference Engine The brain of the ES The control structure (rule interpreter) Provides methodology for reasoning User Interface Inference Engine Knowledge Base

10
**Structure of an Expert System**

User Interface Language processor for friendly, problem-oriented communication Natural Language Processing, or menus and graphics User Interface Inference Engine Knowledge Base

11
**Structure of an Expert System**

Working Memory (Blackboard) Area of working memory to Describe the current problem Record Intermediate results Records Intermediate Hypotheses and Decisions 1. Plan 2. Agenda 3. Solution User Interface Inference Engine Knowledge Base

12
**Structure of an Expert System**

Explanation Subsystem (Justifier) Area of working memory to Traces responsibility and explains the ES behavior by interactively answering questions: -Why? -How? -What? -(Where? When? Who?) User Interface Inference Engine Knowledge Base

13
**Developing Expert Systems**

The Human Element in Expert Systems Expert Knowledge Engineer User Others

14
**Developing Expert Systems**

Has the special knowledge, judgment, experience and methods to give advice and solve problems Provides knowledge about task performance

15
**Developing Expert Systems**

Knowledge Engineer Helps the expert(s) structure the problem area by interpreting and integrating human answers to questions, drawing analogies, posing counterexamples, and bringing to light conceptual difficulties Usually also the System Builder

16
**Developing Expert Systems**

User Possible Classes of Users A non-expert client seeking direct advice (ES acts as a Consultant or Advisor) A student who wants to learn (Instructor) An ES builder improving or increasing the knowledge base (Partner) An expert (Colleague or Assistant) The Expert and the Knowledge Engineer Should Anticipate Users' Needs and Limitations When Designing ES

17
**Architecture of an Expert System**

18
**Knowledge base: production System**

Knowledge is represented using rules of the form : Rule: if Conditions then Conclusions or Rule: if Premises then Actions Rule: if if-part then then-Part A rule as described above is often referred to as a production rule

19
**Knowledge base: production System**

Examples: if symptom1 and symptom2 and symptom3 then disease1 if - the leaves are dry, brittle and discoloured then - the plant has been attacked by red spider mite If – it is raining then – you should take an umbrella if - the customer closes the account then - delete the customer from the database

20
**Inference Engine: Rule-based reasoning**

The essence of a rule-based reasoning system is that it goes through a series of cycles. In each cycle, it attempts to pick an appropriate rule from its collection of rules, depending on the present circumstances, and to use it. Because using a rule produces new information, it's possible for each new cycle to take the reasoning process further than the cycle before. This is rather like a human following a chain of ideas in order to come to a conclusion.

21
**Inference Engine: Forward Chaining**

Forward Chaining is based on Modus Ponens inference rule: (A, AB ) / B In other words, if A is true and we have AB then we can deduce that B is true In the context of Expert System, it is used as follows: if A is in WM and we have a rule in KB of the form if A then B then we can deduce B (add B to WM as new information)

22
**Inference Engine: Forward Chaining**

Do until problem is solved or no antecedents match Collect the rules whose antecedents are found in WM. If more than one rule matches use conflict resolution strategy to eliminate all but one Do actions indicated in by rule “fired” Cycles

23
**Inference Engine: Forward Chaining**

For Conflict Resolution we can use rule-order as an implied priority Matching Rules filtering Conflict Resolution Execution Apply the rule Rules Rule Add Then-Part to WM Cycles

24
**Inference Engine: Forward Chaining**

Algorithm: Match WM with KB to select production rules Eliminate already applied rules If many rules select one which has the smallest number Apply selected rule by adding its conclusion to WM If the problem is solved or no new information added then stop otherwise go to step 1 Matching Rules filtering Conflict Resolution Execution Apply the rule Rules Rule Add Then-Part to WM

25
**Inference Engine: Forward Chaining**

Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Inference Engine: Execute many cycles Working Memory: A, B

26
**Inference Engine: Forward Chaining**

Inference Engine: Execute many cycles Cycle 1: Rule 5 → add C Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C

27
**Inference Engine: Forward Chaining**

Inference Engine: Execute many cycles Cycle 1: Rule 5 → add C Cycle 2: Rule 1, Rule 2, Rule 5 → add D Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C, D

28
**Inference Engine: Forward Chaining**

Inference Engine: Execute many cycles Cycle 1: Rule 5 → add C Cycle 2: Rule 1, Rule 2, Rule 5 → add D Cycle 3: Rule 1, Rule 2, R 3, R 4, Rule 5 → add G Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C, D, G

29
**Inference Engine: Forward Chaining**

Inference Engine: Execute many cycles Cycle 1: Rule 5 → add C Cycle 2: Rule 1, Rule 2, Rule 5 → add D Cycle 3: Rule 1, Rule 2, R3, R 4, Rule 5 → add G Cycle 4: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add F Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C, D, G, F

30
**Inference Engine: Forward Chaining**

Inference Engine: Execute many cycles Cycle 1: Rule 5 → add C Cycle 2:Rule 1, Rule 2, Rule 5 → add D Cylce 3: Rule 1, Rule 2, Rule 3, Rule 5 → add G Cycle 4: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add F Cylce 5: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add E Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C, D, G, F, E

31
**Inference Engine: Forward Chaining**

Inference Engine: Execute many cycles Cycle 1: Rule 5 → add C Cycle 2: Rule 1, Rule 2, Rule 5 → add D Cylce 3: Rule 1, Rule 2, Rule 3, Rule 5 → add G Cycle 4: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add F Cylce 5: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add E Cylce 6: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add Z Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C, D, G, F, E, Z

32
**Inference Engine: Forward Chaining**

Inference Engine: Execute many cycles Cycle 1: Rule 5 → add C Cycle 2:Rule 1, Rule 2, Rule 5 → add D Cylce 3: Rule 1, Rule 2, Rule 3, Rule 5 → add G Cycle 4: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add F Cylce 5: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add E Cylce 6: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → add Z Cycle 7: Rule 1, Rule 2, Rule 3, Rule 4, Rule 5, Rule 6 → no rule => STOP !!! Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B, C, D, G, F, E, Z

33
**Inference Engine: Backward Chaining**

Backward Chaining is based on Modus Tollens inference rule: ( B, AB ) / A If A implies B , and B is false, then A is false In other words, if we have AB then proving B is equivalent of proving A In the context of Expert System, it is used as follows: To prove a goal B which is not in WM and if we have a rule in KB of the form if A then B then we have just to prove A (A is either present in WM or it exists a rule with A as a conclusion)

34
**Inference Engine: Backward Chaining**

Consider the following KB and WM: Inference Engine: Exploration of AND/OR Tree: Suppose we have the goal: G To prove G, one can use Rule 2 Proving G is equivalent of proving B and C As B is present in the WM so it’s true So remains the proof of C, one can use Rule 5 Proving C is equivalent of proving A and B As A and B are present in WM (true), we can deduce that C is true B and C are true then G is true Knowledge Base: Rule 1: If A and B and C then D Rule 2: if B and C then G Rule 3: if B and D then F Rule 4: if B and C and D then E Rule 5: if A and B then C Rule 6: if G and D then Z Working Memory: A, B

35
**Expert System Shells Shell = Inference Engine + User Interface**

ESS allow non programmers to build an expert system, by inserting facts and rules into a generic expert system architecture which is already built. Some ESSs BABYLON JESS ES CLIPS…..

36
**Advantages of expert Systems**

An expert system can operate constantly 24 hours per day. An expert system can exceed the performance of any human expert, as it can combine knowledge from a number of different experts.

37
**Limitations of expert systems**

Not able to cope with unseen information. Not able to cope with noise. Not able to adapt to new environments. Not able to learn independently in a similar manner that humans learn. They need to be programmed in advance.

38
**Conclusions Why use expert systems: Weaknesses:**

commercial viability: whereas there may be only a few experts whose time is expensive and rare, you can have many expert systems expert systems can be used anywhere, anytime expert systems can explain their line of reasoning commercially beneficial: the first commercial product of AI Weaknesses: expert systems are as sound as their KB; errors in rules mean errors in diagnoses automatic error correction, learning is difficult the extraction of knowledge from an expert, and encoding it into machine-inferable form is the most difficult part of expert system implementation

Similar presentations

© 2021 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google