Subject Descriptions - Subject Information

Calendar: 2017 Postgraduate
Faculty: Faculty of Engineering and Information Sciences
Department: School of Computing and Information Technology

Subject Information
Subject Code CSCI924
Subject Name Reasoning and Learning
Credit Points 6
Pre-Requisites None.
Co-Requisites None.
Restrictions None.
Equivalence None.
Assessment Assignments 50% Exam 50%
General Subject No.
EFTSL (Non Weighted) 0.125
Non Weighted Student Contribution Amounts
Commonwealth Supported (HECS) Students Only
Pre-1997 Pre-2005 Post-2005 Post-2008 Post-2009 Post-2010
$ 1131  $ 1131  $ 1131  $ 1131  $ 1131  $ 1131 
Weighted Student Contribution Amounts  
Work Experience No
Tutorial Enrolment Information Students should use the SMP OnLine Tutorial System (via SOLS) to enrol in Tutorial/laboratory groups for this subject. Once enrolments are open a link to the subject will appear in Tutorial Enrolments.

Subject Availability
Session Spring  (24-07-2017 to 16-11-2017)
Campus Wollongong
Delivery Method On Campus
Instance Name Class 1
Course Restrictions No restrictions
Contact Hours 2hr lecture and 1hr tut
Lecturer(s) and
Cons. times
Philip Ogunbona
Coordinator(s) and
Cons. times
Philip Ogunbona
Instance Comment  
Census Date 31-08-2017

Subject Availability
Session CCNU Session 1 2017/2018  (18-09-2017 to 07-01-2018)
Campus Central China Normal University Wuhan
Delivery Method On Campus
Instance Name Class 1
Course Restrictions No restrictions
Contact Hours  
Lecturer(s) and
Cons. times
Qiusha Min
Coordinator(s) and
Cons. times
Fenghui Ren
Luping Zhou
Instance Comment  
Census Date 10-10-2017

Subject Description
This subject introduces students to the concepts of agents and heuristics used in intelligent reasoning and learning systems. Topics covered include multi-agent systems, agent safety, agent liveliness, computational heuristics, machine learning techniques, case based and other forms of knowledge reasoning, temporal reasoning, knowledge extraction, ontology and complexity. It examines software architectures and programming systems for implementing reasoning, learning, searching and modelling to solve intelligent systems' problems in the presence of incomplete information.

Subject Learning Outcomes
On successful completion of this subject, students will be able to:
1. choose an appropriate method to solve an intelligent systems problem
2. design agent-based applications to solve complex problems
3. apply agent algorithms to achieve robust reasoning and decision making
4. understand complexity and how to deal with it using heuristic methods.

Textbook Information

Text book information is available via the UniShop website:

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