units

FIT3080

Faculty of Information Technology

Undergraduate - Unit

print version

6 points, SCA Band 2, 0.125 EFTSL

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered, or view unit timetables.

LevelUndergraduate
FacultyFaculty of Information Technology
OfferedClayton Second semester 2014 (Day)
Malaysia Second semester 2014 (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).

Outcomes

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)

Workload requirements

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

  • Two hours of lectures
  • One 1-hour laboratory

(b.) Additional requirements (all students):

  • A minimum of 9 hours independent study per week for completing lab and project work, private study and revision.

This unit applies to the following area(s) of study

Prerequisites

FIT2004 or CSE2304

Prohibitions

CSE2309, CSE3309, DGS3691

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