Sample Syllabus for an AI Course

Resource Kit
Outfitting A Robot Laboratory
The Robot Exercise

Topics

• Introduction: What is AI? Foundations of AI, History, Intelligence, Agents, Agent Architectures, Environments, LEGO Robots (first view), The Robot Exercise (see below).
• Problem Solving Agents: Problem Formulation, Search (Blind and Informed), Search Algorithms: Breadth-First, Uniform Cost, Depth-First, Limited Depth-First, Iterative Deepening, Bi-directional, Greedy, A*, Heuristics, IDA*, SMA*, Hill Climbing, Simulated Annealing.
• Game Playing: Minimax, Minimax with Alpha-Beta Pruning.
• Knowledge representation I: Basics of Knowledge Representation, Knowledge representation Paradigms, Propositional Logic, Inference Rules in Propositional Logic, First Order Logic, Inference Rules in FOL, Modus Ponens, Proof by Refutation, Resolution.
• Knowledge Representation II: SNePS, SNePSLOG, Semantic Networks in SNePS, Path-Based and Node-Based Inference in SNePS.
• Agents That Communicate: Speech perception, Language Understanding, Language Structure, Transition network Grammars.

Texts

The course used the following texts:

• Stuart Russell & Peter Norvig : Artificial Intelligence: A Modern Approach, Prentice Hall, 1995.
• Kenneth Ford, Clark Glymour, & Patrick Hayes (editors): Android Epistemology, AAAI Press/MIT Press, 1995.
• Stuart C. Shapiro: Common Lisp: An Interactive Approach, Computer Science press, 1992.
• Ivan Bratko: Prolog Programming for Artificial Intelligence, Second Edition, Addison Wesley,1990.

Chapters 1 through 9, and Chapter 22 from Russell & Norvig were covered. Also, the entire Ford, Glymour, and Hayes was covered.

Programming Assignments

In addition to the robot exercises, there were three other programming assignments: Implementing a Hawaiian Checkers playing program using Minimax with Alpha Beta pruning; Doing KR using SNePSLOG; KRR assignment using SNePS semantic networks.