4 points
* Two 1-hour lectures per week
* Second
semester
* Clayton
* Prerequisites: CSC2030, CSC2040, and either
CSC2940 or CSC3940
* Prohibitions: CSC3091, DGS3691, GCO3815, GCO7835,
RDT3691
Objectives On completion of the subject, students should have a working knowledge of basic search techniques, knowledge representation and reasoning mechanisms, and planning systems; be able to analyse problems and determine which artificial intelligence techniques are applicable.
Synopsis Topics include history and philosophy of artificial intelligence; problem solving and search (heuristic search, iterative improvement, game playing); knowledge representation and reasoning (extension of material on propositional and first-order logic for artificial intelligence applications, situation calculus, planning, frames and semantic networks); expert systems overview (production systems, certainty factors); machine learning (decision trees, neural networks, genetic algorithms).
Assessment: Examination (2 hours): 70%
*
Assignments: 30%
Recommended texts
Russell S and Norvig P Artificial intelligence: A modern approach Prentice-Hall, 1994
Back to the Information Technology Handbook, 1998
Published by Monash University, Australia
Maintained by wwwdev@monash.edu.au
Approved by M Rambert, Faculty of Information Technology
Copyright © Monash University 1997 - All Rights Reserved -
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