ARTIFICIAL INTELLIGENCE
			       CSC3091

			Instructor: A. Nicholson

4 points.  Two 1-hour lectures per week.  First semester, Clayton.
Prerequisites: As for CSC3010. Additional prerequisite: CSC2030.
Corequisites: As for CSC3030.  Prohibitions: RDT2821, RDT3501,RDT3691.

Objectives.  On completion of the subject students will be able to:


have a working knowledge of basic search techniques, knowledge
representation and reasoning mechanisms, and planning systems;
have the skills to analyse problems and determine what AI techniques 
are applicable;
write LISP programs for implementing AI problem-solving solutions.  


Synopsis 

This subject covers basic techniques and mechanisms required for the
construction of intelligent agents, with a focus on reasoning and
actions. Topics are as follows: (1) LISP: a functional programming
language commonly used in AI applications. (2) Problem Solving:
constructing search problems; uninformed search techniques
(depth-first, breadth-first, iterative deepening); informed search
(graphsearch, hill-climbing, simulated annealing) including developing
heuristics; game playing (min-max, alpha-beta pruning); strategy
evaluation (completeness, complexity, optimality). (3) Knowledge and
Reasoning: logical reasoning to represent the world, update knowledge,
and deduce actions to achieve goals; predicate calculus, situation
calculus, frames and semantic networks; application using knowledge
and engineering. (4) Planning systems: combining (2) and (3) for
practical planning and scheduling applications; STRIPS representation,
partial-order planning, conditional planning, hierarchical
planning. (5) Uncertainty: reasoning in the presence of uncertainty:
probability theory, belief networks.

Assessment. Examinations: 2 hours; Assignments.

Prescribed texts 

Russell, S and Norvig, P Artificial Intelligence: A modern approach. 
Prentice Hall, 1994.  

Recommended texts

Wilensky, R COMMON Lispcraft, 3rd ed. Norton, 1986.