George Mason University
School of Information Technology and Engineering
Department of Computer Science

CS 785 Knowledge Acquisition and Problem Solving

Meeting time: Thursday 4:30pm – 7:10pm
Meeting location: IN 209

Instructor: Dr. Gheorghe Tecuci, Professor of Computer Science
Office hours: Tuesday and Thursday 3:20pm – 4:20pm
Office: ST-II, Rm. 421
Phone: 703 993 1722
E-mail: tecuci@gmu.edu

Course Description

Prerequisite: an introductory course in artificial intelligence.

The objective of this course is to present the principles and major methods of knowledge acquisition for the development of knowledge-based agents that incorporate the problem solving knowledge of a subject matter expert. Major topics include: overview of knowledge engineering; analysis and modeling of the reasoning process of a subject matter expert; ontology design and development; rule learning; problem solving and knowledge-base refinement. The course will emphasize the most recent advances in this area, such as: agent teaching and learning; mixed-initiative knowledge base refinement; knowledge reuse; frontier research problems.

The students will learn about all the phases of building a knowledge-based agent and will experience them first-hand by using the Disciple agent development environment. Disciple has been developed in the Learning Agents Center of George Mason University and has been successfully used to build knowledge-based agents for a variety of problems, including: planning the repair of damaged bridges and roads; critiquing military courses of action; determining strategic centers of gravity in military conflicts; generating test questions for higher-order thinking skills in history and statistics. The students will use Disciple to develop an intelligent assistant that helps in selecting a Ph.D. Dissertation Advisor. The classes will consist of a theoretical part and a practical part. In the theoretical part, the instructor will present and discuss the various phases and methods of building a knowledge-based agent. In the practical part the students will apply this knowledge to specify, design and develop the assistant for selecting a Ph.D. Dissertation Advisor.

Grading Policy

Exam – 50%
Agent development – 50%

Readings

Tecuci G., Lecture Notes on Knowledge Acquisition and Problem Solving, Fall 2004, available online (required).

Tecuci G., Building Intelligent Agents: An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies, Academic Press, 1998 (recommended).

Additional papers recommended by the instructor.

Lecture Notes on Knowledge Acquisition and Problem Solving

1.      Introduction to intelligent agents and knowledge acquisition, 8-27-04

2.      Overview of knowledge engineering and of the manual knowledge acquisition methods, 9-7-04

3.      Mixed-initiative knowledge acquisition. Overview of the Disciple approach.9-18-04

      The Disciple-RKF Learning and Reasoning Agent, Research Report (submitted for publication), 9-16-04 (Optional reading)

4.      Problem solving through task reduction. Modeling the reasoning of subject matter experts, 9-18-04

4.1 Demo: Using the modeling advisor of Disciple, 10-21-04

5.      Ontology design and development, 10-14-04

6.    Agent problem solving, 10-21-04

7.    Agent teaching and multistrategy learning, 10-21-04

8.      Mixed-initiative problem solving and knowledge base refinement, 10-21-04

9.      Scripts development for scenario elicitation.

10.      Discussion of frontier research problems.