CPE/CSC 481: Knowledge-Based Systems Dr. Franz J. Kurfess Computer Science Department Cal Poly

Usage of the Slides these slides are intended for the students of my CPE/CSC 481 “Knowledge-Based Systems” class at Cal Poly SLO if you want to use them outside of my class, please let me know (fkurfess@calpoly.edu) I usually put together a subset for each quarter as a “Custom Show” to view these, go to “Slide Show => Custom Shows”, select the respective quarter, and click on “Show” To print them, I suggest to use the “Handout” option 4, 6, or 9 per page works fine Black & White should be fine; there are few diagrams where color is important

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

Outlook Knowledge-Based Systems Motivation Objectives Intelligent Agents knowledge representation and reasoning for autonomous agents Semantic Web reasoning with metadata and linked documents Knowledge Management support for knowledge workers KBS at Cal Poly potential use of knowledge-based systems at Cal Poly Important Concepts and Terms Chapter Summary

Logistics Introductions Course Materials Term Project textbooks (see below) lecture notes PowerPoint Slides will be available on my Web page handouts Web page http://www.csc.calpoly.edu/~fkurfess Term Project Lab and Homework Assignments Exams Grading

Bridge-In

Pre-Test

Motivation reasons to study the concepts and methods in the chapter main advantages potential benefits understanding of the concepts and methods relationships to other topics in the same or related courses

Objectives regurgitate understand evaluate apply basic facts and concepts understand elementary methods more advanced methods scenarios and applications for those methods important characteristics differences between methods, advantages, disadvantages, performance, typical scenarios evaluate application of methods to scenarios or tasks apply methods to simple problems

Evaluation Criteria

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

Semantic Web WWW enhanced by meta-data and reasoning infrastructure XML as common base ontologies to define terms and relationships for models description logics as formal foundation Web services via e.g. Simple Object Access Protocol (SOAP) 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), http://www.sciam.com/print_version.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21

Semantic Web Examples IRS Internet Reasoning Service RuleML a Semantic Web services framework http://kmi.open.ac.uk/projects/irs/ RuleML canonical Web language for rules using XML markup, formal semantics, and efficient implementations

IRS Internet Reasoning Service a Semantic Web services framework available at http://kmi.open.ac.uk/projects/irs/ allows applications to semantically describe and execute Web services supports the provision of semantic reasoning services within the context of the Semantic Web. http://kmi.open.ac.uk/projects/irs/

IRS Architecture a server-client based approach IRS Server IRS Publisher IRS Client http://kmi.open.ac.uk/projects/irs/

IRS Component Example http://kmi.open.ac.uk/projects/irs

RuleML covers the entire rule spectrum can specify from derivation rules to transformation rules to reaction rules can specify queries and inferences in Web ontologies mappings between Web ontologies dynamic Web behaviors of workflows, services, and agents further information at the Rule Markup Initiative Web page http://www.ruleml.org/

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

RuleML Example <!-- Implication Rule 1 (permuted): Forward notation of _body and _head roles, similar to Production Systems (role permutation does not affect the semantics) --> <imp> <_body> <and> <atom> <_opr><rel>premium</rel></_opr> <var>customer</var> </atom> <_opr><rel>regular</rel></_opr> <var>product</var> </and> </_body> <_head> <_opr><rel>discount</rel></_opr> <ind>5.0 percent</ind> </_head> </imp> "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 http://www.ruleml.org/lib/discount-variations.ruleml

Ontologies definition of terms and relationships formal foundations, but still accessible for humans usually restricted to specific domains merge aspects of dictionaries taxonomies and hierarchies semantic networks for an introduction, see Ontology Development 101: A Guide to Creating Your First Ontology by Natalya F. Noy and Deborah L. McGuinness, Stanford University, http://www.ksl.stanford.edu/people/dlm/papers/ontology101/ontology101-noy-mcguinness.html

Knowledge Management support for knowledge workers emphasis on knowledge representation and reasoning support for humans knowledge processing by computers is less important

Chaotic vs. Systematic Knowledge Handling heuristics unsound reasoning methods inconsistent knowledge jumping to conclusions ill-defined problems unclear boundaries of knowledge informal, continuous meta-reasoning systematic rules formal logic consistency proofs well-defined problems domain-specific knowledge expensive, distinct meta-reasoning

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

Knowledge-Based Systems at Cal Poly? Based on what you learned in this class, do you see potential uses for knowledge-based systems at Cal Poly? Discuss possible applications in a small group, and post them on the Blackboard discussion forum. domain and application main purpose sources of knowledge suitable KB methods and techniques knowledge representation reasoning benefits problems

KBS @ Cal Poly W06 Student Advising System room scheduling classes, tests, GRW, graduation evaluation, progress tracking room scheduling minor selector optimizing combining majors and minors club matching system housing matching system parking advisor nearest available spot

Questions

Figure Example

Post-Test

Evaluation Criteria

Important Concepts and Terms common-sense knowledge expert system (ES) expert system shell inference inference mechanism If-Then rules knowledge knowledge acquisition knowledge base knowledge-based system knowledge representation production rules reasoning rule

Summary Outlook