Intelligent systems
C Greif
4 points * Second semester * Clayton * Prerequisites: RDT2131 or CSC2040 * Prohibited combination: CSC3091, GCO3815
Computers and cognitive processes, language and programming, perception and neural nets. State-space methods to solve NP problems. Search as a starting point for heuristic programming. Constraint satisfaction, dependency-directed back-tracking, means-ends analysis, depth-, breadth-, best- first search; mini-max, alpha-beta, A* search algorithms; neural nets and optimisation. Introduction to formal logic, logical inference and LUR-resolution, procedural and declarative knowledge representation, default reasoning, semantic nets and frames. Expert systems, forward and backward chaining, management of uncertain knowledge. Machine learning, learning from examples, learning and production systems, discovery of concepts. Applications: machine vision, robotics and STRIPS-like planning, natural language processing and conceptual dependency graphs.
Assessment
Examination: 60% * Assignment and practical work: 40%
Recommended texts
Bratko I Prolog programming for artificial intelligence Addison-Wesley, 1990
Luger G and Stubblefield W Artificial intelligence structures and strategies for complex problem solving 2nd edn, Benjamin-Cummings, 1993
Patterson D W Introduction to artificial intelligence and expert systems Prentice-Hall, 1990
Winston P Artificial intelligence 3rd edn, Addison-Wesley, 1992
Winston P and Horn B K P Lisp Addison-Wesley, 1988