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

© 2005 Franz J. Kurfess Expert System Examples 1

CPE/CSC 481: Knowledge-Based Systems

CPE/CSC 481: Knowledge-Based Systems

Dr. Franz J. Kurfess

Computer Science Department

Cal Poly


Course OverviewCourse 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 SystemsOutlook 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

Important Concepts and Terms

Chapter Summary


LogisticsLogistics Introductions Course Materials

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-InBridge-In


Pre-TestPre-Test


MotivationMotivation

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


ObjectivesObjectives regurgitate

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


Intelligent AgentsIntelligent 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 WebSemantic 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 ExamplesSemantic Web Examples

IRS Internet Reasoning Service 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 ServiceIRS Internet Reasoning Service a Semantic Web services framework available at http://kmi

.open.ac.uk/projects/irs/

http://kmi.open.ac.uk/projects/irs/


IRS ArchitectureIRS Architecture a server-client based approach

IRS Server IRS Publisher IRS Client

http://kmi.open.ac.uk/projects/irs/


RuleMLRuleML

covers the entire rule spectrum 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 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 ExampleRuleML Example<imp> <_body> <and> <atom> <_opr><rel>premium</rel></_opr> <var>customer</var> </atom> <atom> <_opr><rel>regular</rel></_opr> <var>product</var> </atom> </and> </_body> <_head> <atom> <_opr><rel>discount</rel></_opr> <var>customer</var> <var>product</var> <ind>5.0 percent</ind> </atom> </_head></imp>

"The discount for a customer buying a product is 5.0 percentif 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


OntologiesOntologies 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 ManagementKnowledge 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

Chaotic vs. Systematic Knowledge Handling

chaotic 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 FusionKnowledge 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


QuestionsQuestions


Figure ExampleFigure Example


Post-TestPost-Test


Important Concepts and TermsImportant 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 OutlookSummary Outlook

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

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