George Mason University
School of Information Technology and Engineering
Department of Computer Science
CS 785 Knowledge Acquisition and Problem Solving
Meeting time: Monday 7:20pm – 10pm
Meeting location: R A243
Instructor: Dr. Gheorghe Tecuci, Professor of Computer Science
Office: ST-II, Rm. 421
E-mail: tecuci@ gmu.edu
Prerequisite: an introductory course in artificial intelligence or permission of instructor.
The objective of this course is to present principles and major methods of knowledge acquisition for the development of knowledge bases and problem solving agents. Major topics include: overview of knowledge engineering, general problem solving methods, ontology design and development, modeling of the problem solving process, learning strategies, rule learning and rule refinement. The course will emphasize the most recent advances in this area, such as: knowledge reuse, agent teaching and learning, knowledge acquisition directly from subject matter experts, and mixed-initiative knowledge base development. It will also discuss open issues and frontier research.
The students will acquire hands-on experience with a complex, state-of-the-art methodology and tool for the end-to-end development of a knowledge-based agent. The methodology and tool have been developed in the Learning Agents Laboratory of George Mason University and have been successfully used to build knowledge-based agents for a variety of problems: planning the repair of damaged bridges and roads; critiquing military courses of action; identifying strategic center of gravity candidates in military conflicts; generating test questions for higher-order thinking skills in history; and others.
Individual or group projects will be defined, based on the interests of the students and the available tools.
Exam – 50%
Project – 50%
Lecture notes provided by the instructor (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.