CSCI-8050 Knowledge-Based Systems
Prerequisites: (CSCI-6540 and CSCI-6550) or POD.
Description: (Themes: Knowledge & Expertise, AI/DB
Integration, Decision Support)
Theory and practice of knowledge based system construction
with particular emphasis on rule-based expert systems. Topics include KBS
fundamentals, knowledge representation, knowledge base construction, knowledge
integration in databases, inference engines, reasoning from incomplete or
uncertain information, intelligent decision support, and user tools &
interfaces.
Instructor: Walter D. Potter
Office: GSRC-113 (enter through 111)
Phone: 542-0361 (with rollover and voice mail)
Email: potter@uga.edu
Hours: By Appointment, Drop In, or __(hours to be
determined)__
Notes: If you stop-by or call, and I’m NOT available then be
sure to leave a note. I’ll get back to
you as soon as possible. E-mail is best.
Texts (required):
1) Introduction to Expert Systems, Third Edition by
Peter Jackson
2) Microsoft's Age of Mythology (tentative)
References (in Library):
Introduction to Knowledge Systems by Mark Stefik
Intelligent Database Systems by Bertino,
Rule-Based Expert Systems by Buchanan, B.G. and E.H.
Shortliffe, eds.,
PROLOG Programming in Depth by
Reserve Books, and Current Literature
Grading*:
|
ES/IDB/IIS |
60% |
Systems, reports, & presentations (variable due dates) |
|
Assignments |
20% |
Talks, summaries & other HW (due weekly) |
|
Final Exam |
20% |
around Tuesday December 14th, |
*No late coursework accepted. Due dates are scheduled
in advance and are firm.
*Class attendance is required and class participation is
graded (under assignments).
Policies: Note that each student is expected to do
his/her own work. Any evidence of
academic dishonesty will not be tolerated and will be subject to disciplinary
action. Be sure you are familiar with
the University’s academic (dis)honesty policy as well as any departmental
policies (see attached). No make-up
exams are given.
NOTE: The course syllabus provides a general guide for the
course; deviations may be necessary.
CSCI-8050: Knowledge Based Systems
Scope: The road map we plan to follow this semester
includes a focus on three distinct areas of knowledge based systems: expert
systems, intelligent database systems, and intelligent information systems.
Expert Systems are knowledge based systems that attempt to
rival the performance of a human expert. Typically, a knowledge engineering task is
undertaken to acquire expert domain knowledge from one or more human experts. This knowledge is coded using some useful
representation scheme and possibly some expert system shell IDE. We will investigate the development of such a
system (to the extent allowed within our time constraints).
Intelligent Database Systems integrate concepts from AI with
those from the DB arena to form database systems with more capabilities than
merely serving up facts to user queries. Active Databases may be considered a part of
the Intelligent Database Systems domain since they use active triggers (i.e.,
rules) to initiate some internal database processing. Other types of rules may be incorporated into
a database to derive values to "virtual" attributes during query
processing. On another front, rules may
be used to massage a user query in order to provide summary results instead of
some large amount of tabular data.
Intelligent Information Systems bring together several types
of systems to help with the decision making process. A typical IIS has
several components including one or more databases, one or more expert systems,
a structured interface, an intermediate working area (sometimes called a
blackboard), one or more models that can be used for decision making or query
response (i.e., using a forest regeneration simulation model to predict timber
density at some point in the future), and its own processing routines.
These components work together in a transparent fashion to aid user decision
making. The infrastructure to support this seamless interaction among
components is the real heart of an IIS.
TOPICAL OUTLINE
(Each major topic item is covered at the approximate rate
indicated. However, due to the dynamic
nature of the in-class activities, there may be substantial variation from this
schedule.)
Week 1 Expert
Systems - Introduction
Definition
Characteristics
Typical
Applications
Example
Systems
Week 4 Components
of Expert Systems (Architecture)
Knowledge
Base
Knowledge
Representation
Meta-Knowledge
Inference
Engine
Search
Techniques
Reasoning
With Uncertainty
User Interface
User
Dialog
Explanation
Tutoring
Week 9
Tools and Environments for Expert System Development
Week 10 Building
an Expert System
Problem
Selection
Development
Methodology
Knowledge
Acquisition
Pitfalls
Week 12 Evaluation
of Expert Systems
Test
Cases
Refinement
Performance
Week 14 Intelligent
Database Systems
Data
Models
Active
Database Systems
Derivable
Attribute Values
Week 17 Intelligent
Information Systems
Blackboard
Architecture
Wrapper
Architecture
Dependent
Agent Architecture