Department of Computer Science
CS 681 Designing Expert Systems
time: Thursday 4:30 pm – 7:10 pm
Meeting location: The Engineering Building 5358
hours: Thursday 3:30 pm – 4:20 pm
Office: The Engineering Building 4613
Phone: 703 993 1722
E-mail: tecuci at gmu dot edu
Prerequisite: CS 580 or permission of instructor
This course presents the theory and practice of designing and developing systems that rely on expert knowledge and reasoning to solve complex problems in a specific (scientific, engineering, medical, military, etc.) domain. Such an expert system or knowledge-based agent may assist a human expert in complex problem solving and decision-making, may be used by a non-expert user, or may teach problem solving and decision-making to a student. Capturing, using, preserving, transferring, and sharing knowledge is of critical importance to any organization as society evolves from an information society to a knowledge society. Therefore, the ability to design and develop such expert agents for a wide variety of domains is a highly valuable expertise. The course covers both the basics of expert agents and knowledge engineering as well as advanced research topics, and involves the students in expert agents research. Basic topics include modeling expert reasoning, ontology design and development, logic and probabilistic reasoning, knowledge acquisition and learning, knowledge base verification, validation and integration. Advanced topics include mixed-initiative reasoning, agent teaching and multistrategy learning, and collaborative problem solving.
The students will learn about all the phases of building an expert agent and will experience them first-hand by using the Disciple development environment. Disciple has been developed in the Learning Agents Center (http://lac.gmu.edu) of George Mason University and has been successfully used to build expert agents for a variety of domains, including: intelligence analysis, military center of gravity determination, medical diagnosis, website evaluation, course of action critiquing, emergency response planning, teaching critical thinking, 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 agent. In the second part the students will experience the use of advanced artificial intelligence tools for building expert agents. In the project part the students will design and develop an expert agent in a domain of their choice.
Exam – 50%
Project (Expert System Development) – 50%
Tecuci G., Lecture Notes on Designing Expert Systems, Fall 2009 (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 Expert’s 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
You are expected to abide by the GMU honor code. Homework assignments and exams are individual efforts. Information on the university honor code can be found at http://academicintegrity.gmu.edu/honorcode/.
Additional departmental CS information: http://cs.gmu.edu/wiki/pmwiki.php/HonorCode/CSHonorCodePolicies