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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

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cpe csc 481 knowledge based systems

CPE/CSC 481: Knowledge-Based Systems

Dr. Franz J. Kurfess

Computer Science Department

Cal Poly

usage of the slides
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

Course Overview
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

Outlook Knowledge-Based Systems
logistics
Logistics
  • 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
motivation
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
Objectives
  • 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 agents
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
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
Semantic 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 service
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
IRS Architecture
  • a server-client based approach
    • IRS Server
    • IRS Publisher
    • IRS Client

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

irs component example
IRS Component Example

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

ruleml
RuleML
  • 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 example
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>

<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 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
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
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
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

Chaotic vs. Systematic Knowledge Handling
knowledge fusion
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
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
KBS @ Cal Poly W06
  • Student Advising System
    • 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
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

Important Concepts and Terms