George Mason University
School of Information Technology and Engineering
Department of Computer Science
CS 681 Designing
Expert Systems
Meeting time: Tuesday 4:30 pm
– 7:10 pm
Meeting location: IN 326
Instructor: Dr. Gheorghe Tecuci, Professor of
Computer Science
Office hours: Tuesday 3:30 pm
– 4:20 pm
Office: Research I Building, Room 436
Phone: 703 993 1722
E-mail: tecuci at gmu dot edu
Teaching Assistance
Dorin Marcu and Marcel
Barbulescu, Graduate Research Assistants and PhD students
Course
Description
Prerequisite: CS 580 Introduction
to Artificial Intelligence
An
expert system is a software system that incorporates a large amount of human
problem solving expertise in a specific (scientific, engineering, medical,
military, etc.) specialty, allowing it to perform a task that would otherwise
be performed by a human expert. An expert system may support a human expert to
perform a task, may perform an expert task for a non-expert user, or may teach
a user how to perform a task.
The
objective of this course is to present the principles and major methods for
designing and constructing expert systems, and to involve the students in
expert systems research. Major topics include: modeling expertÕs reasoning;
ontology design, development, import and export; knowledge acquisition and
machine learning; agent teaching and multistrategy learning; mixed-initiative
problem-solving; knowledge base refinement; knowledge base verification, validation
and integration; tutoring expert problem solving knowledge; and discussion of
frontier research problems.
The
students will learn about all the phases of building an expert system and will
experience them first-hand by using the Disciple development environment.
Disciple has been developed in the Learning Agents
Center
of George Mason University and has been successfully used to build expert
systems for a variety of domains, including intelligence analysis; military
center of gravity determination; course of action critiquing; emergency
response planning; planning the repair of damaged bridges and roads; teaching
of higher-order thinking skills in history and statistics; and PhD advisor
selection.
The
classes will consist of three parts: theory, tools and project. In the
theoretical part, the instructor will present and discuss the various phases
and methods of building an expert system. In the second part the students will
experience the use of artificial intelligence tools for building expert
systems. In the project part the students will design and develop an assistant
for evaluating the believability of websites.
Grading
Policy
Exam
– 50%
Expert System Development – 50%
Readings
Tecuci
G., Lecture Notes on Designing Expert
Systems, Fall 2008 (required).
Tecuci
G., Building Intelligent Agents: An
Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case
Studies, Academic Press, 1998 (recommended).
Additional
papers required or recommended by the instructor.
Lecture
Notes on Designing Expert Systems
Classical Approaches to the Design
and Development of Expert Systems
Ontology Design and Development
Learning-Oriented Knowledge
Representation
Problem Reduction and Solution
Synthesis
Modeling ExpertÕs Reasoning
Agent Teaching and Multistrategy
Rule Learning
Mixed-Initiative Problem Solving
and Knowledge Base Refinement
Tutoring Expert Problem Solving
Knowledge
Design Principles for Expert
Systems
Frontier Research Problems