Introduction to Expert Systems

1. Introduction to Expert Systems

2. Other Resources • http://www.cee.hw.ac.uk/~alison/ai3notes/chapter2_5.html • Handout at ECE Office

3. What is an Expert System • An Expert System is a computer program that simulates human intelligence and behavior in specific and limited domains • It is composed of three major modules: • A Knowledge Base • An Inference Engine • A User Interface

4. Expert System Major Components

5. Expert Systems are Good For • Limited domains where expert knowledge is available • Providing expert opinion in remote sites • Enhance the performance of tasks by applying heuristic expert knowledge • Planning, Troubleshooting, Robotic manipulation, Exploration, Process Control

6. Expert Systems Are Not Good For • Representing temporal knowledge • Representing spatial knowledge • Performing commonsense reasoning • Recognizing the limits of their ability • Handling inconsistent knowledge

7. Conventional vs. Symbolic Programming • Representations and use of data vs. Representations and use of knowledge • Algorithmic processing vs. Heuristic processing • Repetitive vs. Inferential process • Effective manipulation of large data bases vs. Effective manipulation of large knowledge bases

8. Overall Architecture Working Memory I n t e r f a c e Knowledge Base Inference Engine

9. Terminology • Knowledge Engineering: The discipline of acquiring, encoding and using human domain knowledge to develop a computer application • Expert System: A computer program that uses domain knowledge to perform a specific task usually human experts perform • Knowledge Base: A set of rules and facts describing the domain of an application • Inference Engine: A program that imposes a general control strategy on how the system is working • Working Memory: A set of facts describing a particular consultation • Interface: A program that links the user with the Expert System

10. Knowledge Base • Models domain knowledge usually in the form of rules, frames, or semantic nets • Probabilistic models and fuzzy models can be used to model uncertainty • A typical Expert System has several hundred rules • A Knowledge base can become very complex and its has to be consistent at all times • Knowledge acquisition tools can be used to build and maintain a Knowledge Base

11. Rules IF the infection is pimary-bacteremia AND the site of the culture is one of the sterile sites AND the suspected portal of entry is the gastrointestinal tract THEN there is suggestive evidence (0.7) that infection is bacteroid.

12. Semantic Networks isa has-part Ship has-part isa Ocean Liner Oil Tanker Engine Hull isa isa has-part has-part Swimming Pool Queen Mary Liverpool Boiler

13. Frames Progress Report Author: John Allen (default) Topic: Due Date: Length: 2 pages (default) If-added If-removed isa Author: Mary Smith Topic: Biological Classification Project Due Date: Sept. 30, 2000 Length:40 pages If-needed

14. Inference Engine • Considering that the Knowledge Base encodes domain knowledge and expertise in terms of rules and facts there are three variations for the inference engine: • Forward Chaining or Data Driven (essentially Modus Ponens) • Backward Chaining or Hypothesis Driven • Mixed (i.e. Forward and Backward Chaining combined) • Most Expert Systems assume that the Inference Engine strategy is monotonic • Several Expert Systems allow for reasoning under uncertainty • Probabilistic (MYCIN, Bayes, Demster-Shafer) • Fuzzy • Non-monotonic (Truth Maintenance Systems)

15. Inference and Logic • Modus Ponens: A1, A2, A1 & A2 => B B • Modus Tolens: not A2, A1 => A2 not A1

16. Issues on Building Expert Systems • Lack of Resources • Personnel • Expert System tools • Inherent limitations of Expert System tools • Performing knowledge acquisition • Refining Knowledge Bases • Handling mixed representation schemes • Expert Systems take long time to build

17. Pitfalls in Choosing Problems • Difficult problem chosen and inadequate resources available • The problem the Expert System is about to solve does not warrant the development effort • The problem the Expert System addresses is very general or complex

18. Choosing a Problem • The need for a solution must justify the costs involved in development. There must be a realistic assessment of the costs and benefits involved. • Human expertise is not available in all situations where it is needed. If the ``expert'' knowledge is widely available it is unlikely that it will be worthdeveloping an expert system. • The problem may be solved using symbolic reasoning techniques. It shouldn'trequire manual dexterity or physical skill. • The problem is well structured and does not require (much) common sense knowledge. Common sense knowledge is notoriously hard to capture andrepresent. It turns out that highly technical fields are easier to deal with, and tend to involve relatively small amounts of well formalised knowledge. • The problem cannot be easily solved using more traditional computing methods. If there's a good algorithmic solution to a problem, you don't wantto use an expert system. • Cooperative and articulate experts exist. For an expert system project to be successful it is essential that the experts are willing to help, and don't feelthat their job is threatened! You also need any management and potential users to be involved and have positive attitudes to the whole thing. • .The problem is of proper size and scope. Typically you need problems that require highly specialized expertise, but would only take a human expert ashort time to solve (say an hour, max).

19. Pitfalls on Resource Planning and Choosing Tools • Adding personnel in order to speed up development time • Management perceives that Expert Systems are “just another computer program” • Difficult to model the domain using the chosen tool • Knowledge engineer picks a tool that he/she is familiar without considering the problem specifics • Decision to built the system using a conventional programming language • The shell or tool that is used to build the system is not reliable

20. Pitfalls Dealing with Expert • Interactions with expert are laborious with small payoff • The expert can not find enough time for the interviews • The experts can not understand the rules and the models used • The rules provided by the expert are simplistic • The expert is not excited about helping develop the system • The expert is not familiar with computers and doubts that a computer program can address the problem • So many experts involved, the Knowledge Engineer has difficulty modeling the domain

21. Pitfalls in the Development Process • The expert knowledge and the inference strategy are not distinct • After initial development the Knowledge Engineer finds that important parts of the domain are not addressed • The system has been built using a shell that does not provide explanation facilities • The system contains a very large number of specialized rules and its time related performance is poor • The system during testing is found to be of low quality • The users do not understand error messages and can not edit the rule base • Addition of new rules produces more errors that it fixes