© 2005 franz j. kurfess expert system examples 1 cpe/csc 481: knowledge-based systems dr. franz j....
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© 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
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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
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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
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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
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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
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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
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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)
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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
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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
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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/
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IRS ArchitectureIRS Architecture a server-client based approach
IRS Server IRS Publisher IRS Client
http://kmi.open.ac.uk/projects/irs/
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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/
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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
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RuleML ExampleRuleML 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> <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
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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
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Knowledge ManagementKnowledge Management
support for knowledge workers emphasis on knowledge representation and
reasoning support for humans knowledge processing by computers is less important
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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
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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
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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