© 2005 Franz J. Kurfess Expert System Examples 1 CPE/CSC 481: Knowledge-Based Systems Dr. Franz J. Kurfess Computer Science Department Cal Poly

© 2005 Franz J. Kurfess Expert System Examples 2 Course Overview u Introduction u Knowledge Representation u Semantic Nets, Frames, Logic u Reasoning and Inference u Predicate Logic, Inference Methods, Resolution u Reasoning with Uncertainty u Probability, Bayesian Decision Making u Expert System Design u ES Life Cycle u CLIPS Overview u Concepts, Notation, Usage u Pattern Matching u Variables, Functions, Expressions, Constraints u Expert System Implementation u Salience, Rete Algorithm u Expert System Examples u Conclusions and Outlook

© 2005 Franz J. Kurfess Expert System Examples 3 Outlook Knowledge-Based Systems u Motivation u Objectives u Intelligent Agents u knowledge representation and reasoning for autonomous agents u Semantic Web u reasoning with metadata and linked documents u Knowledge Management u support for knowledge workers u Important Concepts and Terms u Chapter Summary

© 2005 Franz J. Kurfess Expert System Examples 4 Logistics u Introductions u Course Materials u textbooks (see below) u lecture notes u PowerPoint Slides will be available on my Web page u handouts u Web page u u Term Project u Lab and Homework Assignments u Exams u Grading

© 2005 Franz J. Kurfess Expert System Examples 5 Bridge-In

© 2005 Franz J. Kurfess Expert System Examples 6 Pre-Test

© 2005 Franz J. Kurfess Expert System Examples 7 Motivation u reasons to study the concepts and methods in the chapter u main advantages u potential benefits u understanding of the concepts and methods u relationships to other topics in the same or related courses

© 2005 Franz J. Kurfess Expert System Examples 8 Objectives u regurgitate u basic facts and concepts u understand u elementary methods u more advanced methods u scenarios and applications for those methods u important characteristics v differences between methods, advantages, disadvantages, performance, typical scenarios u evaluate u application of methods to scenarios or tasks u apply u methods to simple problems

© 2005 Franz J. Kurfess Expert System Examples 9 Evaluation Criteria

© 2005 Franz J. Kurfess Expert System Examples 10

© 2005 Franz J. Kurfess Expert System Examples 11 Intelligent Agents u autonomous agents with knowledge-handling capabilities u knowledge representation and reasoning is often used for model building and decision making u exchange of knowledge among agents u relatively easy when agents use the same representation and reasoning method v still significant problems since their knowledge bases are not necessarily designed for exchange u use of specific knowledge exchange languages v Knowledge Query and Manipulation Language (KQML) v ontology-based approaches (RDF, OWL, Semantic Web)

© 2005 Franz J. Kurfess Expert System Examples 12 Semantic Web u WWW enhanced by meta-data and reasoning infrastructure u XML as common base u ontologies to define terms and relationships for models u description logics as formal foundation u Web services via e.g. Simple Object Access Protocol (SOAP) u see the Scientific American article “The Semantic Web -- A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities” by Tim Berners-Lee, James Hendler and Ora Lassila (May 2001),

© 2005 Franz J. Kurfess Expert System Examples 13 Semantic Web Examples u IRS Internet Reasoning Service u a Semantic Web services framework u RuleML u canonical Web language for rules using XML markup, formal semantics, and efficient implementations

© 2005 Franz J. Kurfess Expert System Examples 14 IRS Internet Reasoning Service u a Semantic Web services framework available at

© 2005 Franz J. Kurfess Expert System Examples 15 IRS Architecture u a server-client based approach v IRS Server v IRS Publisher v IRS Client

© 2005 Franz J. Kurfess Expert System Examples 16 RuleML u covers the entire rule spectrum u from derivation rules to transformation rules to reaction rules u can specify u queries and inferences in Web ontologies u mappings between Web ontologies u dynamic Web behaviors of workflows, services, and agents u further information at the Rule Markup Initiative Web page

© 2005 Franz J. Kurfess Expert System Examples 17 RuleML Rules u rule interoperation between u industry standards v such as JSR 94, SQL'99, OCL, BPMI, WSFL, XLang, XQuery, RQL, OWL, DAML-S, and ISO Prolog u established systems v CLIPS, Jess, ILOG JRules, Blaze Advisor, Versata, MQWorkFlow, BizTalk, Savvion, etc. u modular RuleML specification and transformations u from and to other rule standards/systems u rules can be stated u in natural language u in some formal notation u in a combination of both

© 2005 Franz J. Kurfess Expert System Examples 18 RuleML Example <!-- Implication Rule 1 (permuted): Forward notation of _body and _head roles, similar to Production Systems (role permutation does not affect the semantics) --> premium customer regular product discount customer product 5.0 percent "The discount for a customer buying a product is 5.0 percent if the customer is premium and the product is regular." Note: This is one of several possible variations

© 2005 Franz J. Kurfess Expert System Examples 19 Ontologies u definition of terms and relationships u formal foundations, but still accessible for humans u usually restricted to specific domains u merge aspects of v dictionaries v taxonomies and hierarchies v semantic networks u for an introduction, see u Ontology Development 101: A Guide to Creating Your First Ontology by Natalya F. Noy and Deborah L. McGuinness, Stanford University, mcguinness.html mcguinness.html

© 2005 Franz J. Kurfess Expert System Examples 20 Knowledge Management u support for knowledge workers u emphasis on knowledge representation and reasoning support for humans u knowledge processing by computers is less important

© 2005 Franz J. Kurfess Expert System Examples 21 Chaotic vs. Systematic Knowledge Handling u chaotic u heuristics u unsound reasoning methods u inconsistent knowledge u jumping to conclusions u ill-defined problems u unclear boundaries of knowledge u informal, continuous meta- reasoning u systematic u rules u formal logic u consistency u proofs u well-defined problems u domain-specific knowledge u expensive, distinct meta- reasoning

© 2005 Franz J. Kurfess Expert System Examples 22 Knowledge Fusion u integration of human-generated and machine- generated knowledge u sometimes also used to indicate the integration of knowledge from different sources, or in different formats u can be both conceptually and technically very difficult u different “spirit” of the knowledge representation used u different terminology u different categorization criteria u different representation and processing mechanisms u ontologies attempt to build bridges u agreements over basic terms, relationships

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© 2005 Franz J. Kurfess Expert System Examples 24 Questions

© 2005 Franz J. Kurfess Expert System Examples 25 Figure Example

© 2005 Franz J. Kurfess Expert System Examples 26 Post-Test

© 2005 Franz J. Kurfess Expert System Examples 27 Evaluation u Criteria

© 2005 Franz J. Kurfess Expert System Examples 28 Important Concepts and Terms u common-sense knowledge u expert system (ES) u expert system shell u inference u inference mechanism u If-Then rules u knowledge u knowledge acquisition u knowledge base u knowledge-based system u knowledge representation u production rules u reasoning u rule

© 2005 Franz J. Kurfess Expert System Examples 29 Summary Outlook

© 2005 Franz J. Kurfess Expert System Examples 30