A part of these slides have been obtained and adapted with .ppt

1、COMP 4200: Expert Systems,Dr. Christel Kemke Department of Computer Science University of Manitoba,A part of the course slides have been obtained and adapted with permission from Dr. Franz Kurfess, CalPoly, San Luis Obispo,General Info,Course Material Course web page: http:/www.cs.umanitoba.ca/comp4

2、200 Textbooks (see below) Lecture Notes* PowerPoint Slides available on the course web page Will be updated during the term if necessary Assessment Lab and Homework Assignments Individual Research Report Group Project Final Exam,Instructor Info,Dr. Christel Kemke E2-412 EITC Building Phone: 474-8674

3、 E-mail: ckemkecs.umanitoba.ca Home page: www.cs.umanitoba.ca/ckemke Office hours: M, W: 12:30-1:30pm T, Th: 11:30-12:30pm,Course Overview,Introduction CLIPS Overview Concepts, Notation, Usage Knowledge Representation Semantic Nets, Frames, Logic Reasoning and Inference Predicate Logic, Inference Me

4、thods, Resolution Reasoning with Uncertainty Probability, Bayesian Decision Making,Pattern Matching Variables, Functions, Expressions, Constraints Expert System Design XPS Life Cycle Expert System Implementation Salience, Rete Algorithm Expert System Examples Conclusions and Outlook,Course Overview,

5、Introduction CLIPS Overview Concepts, Notation, Usage Knowledge Representation Semantic Nets, Frames, Logic Reasoning and Inference Predicate Logic, Inference Methods, Resolution Reasoning with Uncertainty Probability, Bayesian Decision Making,Pattern Matching Variables, Functions, Expressions, Cons

6、traints Expert System Design ES Life Cycle Expert System Implementation Salience, Rete Algorithm Expert System Examples Conclusions and Outlook,Textbooks,Main Textbook Joseph Giarratano and Gary Riley. Expert Systems - Principles and Programming. 4th ed., PWS Publishing, Boston, MA, 2004 Secondary T

7、extbook Peter Jackson. Introduction to Expert Systems. 3rd ed., Addison-Wesley, 1999.,Overview Introduction,Motivation Objectives What is an Expert System (XPS)? knowledge, reasoning General Concepts and Characteristics of XPS knowledge representation, inference, knowledge acquisition, explanation,X

8、PS Technology XPS Tools shells, languages XPS Elements facts, rules, inference mechanism Important Concepts and Terms Chapter Summary,Motivation,utilization of computers to deal with knowledge quantity of knowledge increases rapidly knowledge might get lost if not captured relieves humans from tedio

9、us tasks computers have special requirements for dealing with knowledge acquisition, representation, reasoning some knowledge-related tasks can be solved better by computers than by humans cheaper, faster, easily accessible, reliable,Objectives,to know and comprehend the main principles, components,

10、 and application areas for expert systems to understand the structure of expert systems knowledge base, inference engine to be familiar with frequently used methods for knowledge representation and reasoning in computers to apply XPS techniques for specific tasks application of methods in certain sc

11、enarios,Expert Systems (XPS),rely on internally represented knowledge to perform tasks utilizes reasoning methods to derive appropriate new knowledge are usually restricted to a specific problem domain some systems try to capture more general knowledge General Problem Solver (Newell, Shaw, Simon) Cy

12、c (Lenat),What is an “Expert System”?,A computer system that emulates the decision-making ability of a human expert in a restricted domain Giarratano & Riley 1998 Edward Feigenbaum “An intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enou

13、gh to require significant human expertise for their solutions.” Giarratano & Riley 1998 Sometimes, we also refer to knowledge-based system,Main Components of an XPS,User Interface,Knowledge Base,Inference Engine,Expertise,User,Knowledge / Rules,Facts / Observations,Main XPS Components,knowledge base

14、 contains essential information about the problem domain often represented as facts and rules inference engine mechanism to derive new knowledge from the knowledge base and the information provided by the user often based on the use of rules user interface interaction with end users development and

15、maintenance of the knowledge base,Concepts and Characteristics of XPS,knowledge acquisition transfer of knowledge from humans to computers sometimes knowledge can be acquired directly from the environment machine learning, neural networks knowledge representation suitable for storing and processing

16、knowledge in computers inference mechanism that allows the generation of new conclusions from existing knowledge in a computer explanation illustrates to the user how and why a particular solution was generated,Dieng et al. 1999,Development of XPS Technology,strongly influenced by cognitive science

17、and mathematics / logic the way humans solve problems formal foundations, especially logic and inference production rules as representation mechanism IF THEN type rules reasonably close to human reasoning can be manipulated by computers appropriate granularity knowledge “chunks” are manageable for h

18、umans and computers,Rules and Humans,rules can be used to formulate a theory of human information processing (Newell “thinking”) a cognitive processor combines evidence from currently active rules this model is the basis for the design of many rule-based systems (production systems),Early XPS Succes

19、s Stories,DENDRAL (Feigenbaum, Lederberg, and Buchanan, 1965) deduce the likely molecular structure of organic chemical compounds from known chemical analyses and mass spectrometry data MYCIN (Buchanan and Shortliffe, 1972-1980) diagnosis of infectious blood diseases and recommendation for use of an

20、tibiotics “empty” MYCIN = EMYCIN = XPS shell PROSPECTOR analysis of geological data for minerals discovered a mineral deposit worth $100 million XCON/R1 (McDermott, 1978) configuration of DEC VAX computer systems 2500 rules; processed 80,000 orders by 1986; saved DEC $25M a year,The Key to XPS Succe

21、ss,convincing ideas rules, cognitive models practical applications medicine, computer technology, separation of knowledge and inference expert system shell allows the re-use of the “machinery” for different domains concentration on domain knowledge general reasoning is too complicated,When (Not) to

22、Use an XPS,Expert systems are not suitable for all types of domains and tasks They are not useful or preferable, when efficient conventional algorithms are known the main challenge is computation, not knowledge knowledge cannot be captured efficiently or used effectively users are reluctant to apply

23、 an expert system, e.g. due to criticality of task, high risk or high security demands,XPS Development Tools,XPS shells an XPS development tool / environment where the user provides the knowledge base CLIPS, JESS, EMYCIN, Babylon, . Knowledge representation languages; ontologies higher-level languag

24、es specifically designed for knowledge representation and reasoning KRL, KQML, KIF, DAML, OWL, Cyc,XPS Elements,knowledge base inference engine working memory agenda explanation facility knowledge acquisition facility user interface,XPS Structure,Knowledge Base,Inference Engine,Working Memory,User I

25、nterface,Agenda,Knowledge Base(rules),Explanation Facility,User Interface,Knowledge Acquisition Facility,Working Memory(facts),XPS Structure,Architecture of Rule-Based XPS 1,Knowledge-Base / Rule-Base store expert knowledge as condition-action-rules (aka: if-then- or premise-consequence-rules) Worki

26、ng Memory stores initial facts and generated facts derived by inference engine; maybe with additional parameters like the “degree of trust” into the truth of a fact certainty factor,Architecture of Rule-Based XPS 2,Inference Engine matches condition-part of rules against facts stored in Working Memo

27、ry (pattern matching); rules with satisfied condition are active rules and are placed on the agenda; among the active rules on the agenda, one is selected (see conflict resolution, priorities of rules) as next rule for execution (“firing”) consequence of rule is added as new fact(s) to Working Memor

28、y,Architecture of Rule-Based XPS 3,Inference Engine + additional components might be necessary for other functions, like calculation of certainty values, determining priorities of rules, conflict resolution mechanisms, a truth maintenance system (TMS) if reasoning with defaults and beliefs is reques

29、ted,Architecture of Rule-Based XPS 4,Explanation Facilityprovides justification of solution to user (reasoning chain) Knowledge Acquisition Facilityhelps to integrate new knowledge; also automated knowledge acquisition User Interfaceallows user to interact with the XPS - insert facts, query the syst

30、em, solution presentation,Rule-Based XPS,knowledge is encoded as IF THEN rules Condition-action pairs the inference engine determines which rule antecedents (condition-part) are satisfied the left-hand condition-part must “match” facts in the working memory matching rules are “activated”, i.e. place

31、d on the agenda rules on the agenda can be executed (“fired”) an activated rule may generate new facts and/or cause actions through its right-hand side (action-part) the activation of a rule may thus cause the activation of other rules through added facts based on the right-hand side of the fired ru

32、le,Example Rules,Production Rules the light is red = stopthe light is green = go,antecedent (left-hand-side),consequent (right-hand-side),IF THEN Rules Rule: Red_LightIF the light is redTHEN stop Rule: Green_LightIF the light is greenTHEN go,antecedent(left-hand-side),consequent (right-hand-side),MY

33、CIN Sample Rule,Human-Readable Format IF the stain of the organism is gram negative AND the morphology of the organism is rod AND the aerobiocity of the organism is gram anaerobic THEN there is strong evidence (0.8) that the class of the organism is enterobacteriaceae,MYCIN Format IF (AND (SAME CNTE

34、XT GRAM GRAMNEG)(SAME CNTEXT MORPH ROD)(SAME CNTEXT AIR AEROBIC) THEN (CONCLUDE CNTEXT CLASS ENTEROBACTERIACEAE TALLY .8),Durkin 94, p. 133,Inference Engine Cycle,describes the execution of rules by the inference engine “recognize-act cycle” pattern matching update the agenda (= conflict set) add ru

35、les, whose antecedents are satisfied remove rules with non-satisfied antecedents conflict resolution select the rule with the highest priority from the agenda execution perform the actions in the consequent part of the selected rule remove the rule from the agenda the cycle ends when no more rules a

36、re on the agenda, or when an explicit stop command is encountered,Forward and Backward Chaining,different methods of reasoning and rule activation forward chaining (data-driven) reasoning from facts to the conclusion as soon as facts are available, they are used to match antecedents of rules a rule

37、can be activated if all parts of the antecedent are satisfied often used for real-time expert systems in monitoring and control examples: CLIPS, OPS5 backward chaining (query-driven) starting from a hypothesis (query), supporting rules and facts are sought until all parts of the antecedent of the hy

38、pothesis are satisfied often used in diagnostic and consultation systems examples: EMYCIN,Foundations of Expert Systems,Rule-Based Expert Systems,Knowledge Base,Inference Engine,Rules,Pattern Matching,Facts,Rete Algorithm,Markov Algorithm,Post Production Rules,Conflict Resolution,Action Execution,Po

39、st Production Systems,production rules were used by the logician Emil L. Post in the early 40s in symbolic logic Posts theoretical result any system in mathematics or logic can be written as a production system basic principle of production rules a set of rules governs the conversion of a set of str

40、ings into another set of strings these rules are also known as rewrite rules simple syntactic string manipulation no understanding or interpretation is required,Markov Algorithms,in the 1950s, A. A. Markov introduced priorities as a control structure for production systems rules with higher prioriti

41、es are applied first allows more efficient execution of production systems but still not efficient enough for expert systems with large sets of rules,Rete Algorithm,Rete is a Latin word and means network, or net The Rete Algorithm was developed by Charles L. Forgy in the late 70s for CMUs OPS (Offic

42、ial Production System) shell stores information about the antecedents in a network in every cycle, it only checks for changes in the networks this greatly improves efficiency,XPS Advantages,economical lower cost per user availability accessible anytime, almost anywhere response time often faster tha

43、n human experts reliability can be greater than that of human experts no distraction, fatigue, emotional involvement, explanation reasoning steps that lead to a particular conclusion intellectual property cant walk out of the door,XPS Problems,limited knowledge “shallow” knowledge no “deep” understa

44、nding of the concepts and their relationships no “common-sense” knowledge no knowledge from possibly relevant related domains “closed world” the XPS knows only what it has been explicitly “told” it doesnt know what it doesnt know mechanical reasoning may not have or select the most appropriate metho

45、d for a particular problem some “easy” problems are computationally very expensive lack of trust users may not want to leave critical decisions to machines,Summary Introduction,expert systems or knowledge based systems are used to represent and process knowledge in a format that is suitable for comp

46、uters but still understandable by humans If-Then rules are a popular format the main components of an expert system are knowledge base inference engine XPS can be cheaper, faster, more accessible, and more reliable than humans XPS have limited knowledge (especially “common-sense”), can be difficult

47、and expensive to develop, and users may not trust them for critical decisions,Important Concepts and Terms,agenda backward chaining common-sense knowledge conflict resolution expert system (XPS) expert system shell explanation forward chaining inference inference mechanism If-Then rules knowledge kn

48、owledge acquisition,knowledge base knowledge-based system knowledge representation Markov algorithm matching Post production system problem domain production rules reasoning RETE algorithm rule working memory,References,DENDRAL, MYCIN, etc. http:/www.nap.edu/readingroom/books/far/ch9_b3.html R1/XCON http:/en.wikipedia.org/wiki/Xcon,