COM S 572
Offered during Fall Semester each year.
- Credits and contact hours: 3 credits, 4 contact hours
- Instructor’s or course coordinator’s name: Yan-Bin Jia, Jin Tian
- Text book, title, author, and year: Artificial Intelligence: A Modern Approach, 4th edition, Stuart Russell and Peter Norvig.
- Other supplemental materials: None
Specific course information
- Brief description of the content of the course: Specification, design, implementation, and selected applications of intelligent software agents and multi-agent systems. Computational models of intelligent behavior, including problem solving, knowledge representation, reasoning, planning, decision making, learning, perception, action, communication and interaction. Reactive, deliberative, rational, adaptive, learning and communicative agents and multiagent systems. Artificial intelligence programming. Research project and written report required for graduate credit.
- Prerequisites or co-requisites: COM S 311, STAT 305 or STAT 330, ENGL 250, SP CM 212; for graduate credit: graduate standing or permission of instructor
- Required, elective, or selected elective? Selected elective
Specific goals for the course
- Specific outcomes of instruction:
- Appreciation of fundamental problems in artificial intelligence (AI).
- Ability to generate precise formulation(s) of AI problems in terms of knowledge representation and search from imprecise English description(s).
- Ability to design intelligent agents for problem solving, reasoning, planning, decision making, and learning. (1, 2)
- Ability to make intelligent choices from among available algorithms and knowledge representation schemes subject to specific design and performance constraints, and when needed, design variants of existing algorithms. (6)
- Ability to implement and evaluate intelligent agents for representative AI problems – e.g., automated theorem proving, learning classification rules from data, etc. (2)
- Familiarity with some current applications of AI.
- Ability to communicate effectively about AI problems, algorithms, implementations, and their experimental evaluation.
Brief list of topics to be covered
- Overview of the course, overview of artificial intelligence, overview of intelligent agents
- Problem-solving and Search: Uninformed search, heuristic search, A*, local search, constraint satisfaction problem, games
- Knowledge Representation: Logical Agents, Propositional logic, First-Order Logic, Inference in First-Order Logic
- Uncertain Knowledge and Reasoning: Probability theory, Probabilistic Reasoning, Bayesian networks
- Learning Agents: Statistical learning, Bayesian decision theory, nearest neighbor classifiers, Naive Bayes model, Decision tree classifiers, Neural networks
- Selected Topics (As Time Permits): Planning Agents, Reasoning under assumptions, Reasoning about Knowledge, Customizing information retrieval, Automated knowledge discovery for diagnostics