Leader: Professor Mark Wallace

Offered

Clayton First semester 2008 (Day)

Synopsis

The objective is to understand the role of intelligent decision support in organisations, paradigms and applications, dealing with uncertain data; system design and construction; to recognise of the value of intelligent decision support, to adopt a critical approach to the choice of method, to appreciate the impact of data quality, and business constraints on the behaviour of a decision support system, and the limitations of formal decision models; to separate modelling from solving, implement simple decision support tools on a constraint programming platform, combine methods to meet application requirements, and to assess the limitations in scalability and precision of a solution.

Objectives

To acquire Knowledge and Understanding of:

1. The role of intelligent decision support in organisations;
2. Decision support paradigms and applications;
3. Methods for handling certain and uncertain knowledge;
4. Issues in the design and construction of intelligent decision support systems;
5. Correctness, precision and scalability.

To develop the following Attitudes, Values and Beliefs:

1. Recognition of the value of intelligent decision support within an organisation;
2. Adoption of a critical approach to the choice of decision support method;
3. Appreciation of the impact of data quality, and business constraints on the behaviour of a decision support system;
4. Appreciation of the limitations of formal decision models and the handling of uncertainty.

To develop the following Practical Skills:

1. Choose appropriate decision support methods;
2. Separate modelling from solving;
3. Implement simple decision support tools on a constraint programming platform;
4. Combine methods to meet application requirements;
5. Assess the limitations in scalability and precision of a solution.

In addition, it is expected that the following Relationships, Communication and Team Work skills will be developed and enhanced:

1. Document and communicate an intelligent decision support model;
2. Work in a team during model design and implementation stages;
3. Present a justification for choosing or combining decision support methods.

Assessment

Examination: 60%
Assignments, class tests and laboratory exercises: 40%
Students must pass the examination in order to pass the unit.

Contact hours

4 x contact hrs/week

Prerequisites

FIT1006 or ETC1000 and 24 points at first years