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

2. 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
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

3. 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

4. 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

5. Logistics Introductions Course Materials Term Project
textbooks (see below)
lecture notes
PowerPoint Slides will be available on my Web page
handouts
Web page
Term Project
Lab and Homework Assignments
Exams
Grading

6. Bridge-In

7. Pre-Test

8. 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

9. 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

10. Evaluation Criteria

12. 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)

13. 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),

14. Semantic Web Examples IRS Internet Reasoning Service RuleML
a Semantic Web services framework
RuleML
canonical Web language for rules using XML markup, formal semantics, and efficient implementations

15. IRS Internet Reasoning Service
a Semantic Web services framework available at
allows applications to semantically describe and execute Web services
supports the provision of semantic reasoning services within the context of the Semantic Web.

16. IRS Architecture a server-client based approach IRS Server
IRS Publisher
IRS Client

17. IRS Component Example

18. 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

19. 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

20. 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

21. 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,

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

23. 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

24. 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

25. 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

26. 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

27. Questions

28. Figure Example

29. Post-Test

30. Evaluation
Criteria

31. 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

32. Summary Outlook