Monash University Handbooks 2008

FIT3080 - Artificial intelligence

6 points, SCA Band 2, 0.125 EFTSL

Undergraduate Faculty of Information Technology

Offered

Clayton First semester 2008 (Day)
Sunway Second semester 2008 (Day)

Synopsis

This unit includes history and philosophy of artificial intelligence; intelligent agents; problem solving and search (problem representation, 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); reasoning under uncertainty (belief networks compared to other approaches such as fuzzy logic); machine learning (decision trees, neural networks, genetic algorithms).

Objectives

At the completion of this unit students will have knowledge and understanding of:

  1. the historical and conceptual development of AI;
  2. the goals of AI and the main paradigms for achieving them, including logical inference, search, nonmonotonic logics, neural network methods and Bayesian inference;
  3. the social and economic roles of AI;
  4. heuristic AI for problem solving;
  5. basic knowledge representation and reasoning mechanisms;
  6. automated planning and decision-making systems;
  7. probabilistic inference for reasoning under uncertainty;
  8. machine learning techniques and their uses;
  9. foundational issues for AI, including the frame problem and the Turing test;
  10. AI programming techniques;

At the completion of this unit students will have developed attitudes that enable them to:
  1. appreciate the potential and limits of the main approaches to AI;
  2. be ready to reason critically about claims of the effectiveness of AI programs.

At the completion of this unit students will have the skills to:
  1. analyse problems and determine where AI techniques are applicable;
  2. implement AI problem-solving techniques in Lisp;
  3. compare AI techniques in terms of complexity, soundness and completeness.

Assessment

Assignments: 40%; Examination (3 hours): 60%.

Contact hours

3 x contact hrs/week

Prerequisites

FIT2004 or CSE2304

Prohibitions

CSC2091, CSC3091, CSE2309, CSE3309, DGS3691, GCO3815, GCO7835, RDT3691

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