units

FIT3080

Faculty of Information Technology

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Monash University Handbook 2010 Undergraduate - Unit

6 points, SCA Band 2, 0.125 EFTSL

LevelUndergraduate
FacultyFaculty of Information Technology
OfferedClayton Second semester 2010 (Day)
Sunway Second semester 2010 (Day)
Coordinator(s)Dr Kevin Korb (Clayton); Dr Simon Egerton (Malaysia)

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 -
A knowledge and understanding of:

  • the historical and conceptual development of AI;
  • the goals of AI and the main paradigms for achieving them including logical inference, search, nonmonotonic logics, neural network methods and Bayesian inference;
  • the social and economic roles of AI;
  • heuristic AI for problem solving;
  • basic knowledge representation and reasoning mechanisms;
  • automated planning and decision-making systems;
  • probabilistic inference for reasoning under uncertainty;
  • machine learning techniques and their uses;
  • foundational issues for AI, including the frame problem and the Turing test;
  • AI programming techniques.
Developed attitudes that enable them to:
  • appreciate the potential and limits of the main approaches to AI;
  • be ready to reason critically about claims of the effectiveness of AI programs;
  • analyse problems and determine where AI techniques are applicable;
  • implement AI problem-solving techniques in Lisp;
  • compare AI techniques in terms of complexity, soundness and completeness.

Assessment

Examination (3 hours): 60%; In-semester assessment: 40%

Chief examiner(s)

Dr Kevin Korb

Contact hours

2 hrs lectures/wk, 1 hr laboratory/wk

Prerequisites

FIT2004 or CSE2304

Prohibitions

CSE2309, CSE3309, DGS3691

Additional information on this unit is available from the faculty at:

http://www.infotech.monash.edu.au/units/fit3080