Who are the potential stakeholders, who was consulted about the
proposal (inside the School as well as outside), what was the
result of that consultation?
The main consultation was held with the Artificial Intelligence researchers
within the department.
Ron van der Meyden
Likely enrolment (with justification), and impact on enrolments of
We expect that a fourth to a third of the attendants of the three offered
AI courses attend this course, so this course should have the same size as
the introductury AI courses. It might impact other advanced AI courses
such as COMP9417 Machine Learning, COMP9441 Cryptography and Security,
COMP9444 Neural Networks, COMP9511 Human-Computer Interaction, COMP9517
Computer Vision, COMP9518 Pattern Recognition and Vision in that students
who want to attend further AI courses decide to take our course instead of
one of the others.
Justification of Prerequisites (or lack thereof)
At least 12CP in COMP3xxx courses or above including
one of the introductory AI courses COMP3411, COMP4415,
Any Courses this is Replacing, and Why?
Delivery and Assessment
3 hours per week.
4 assignments (15% each)
1 project, can be done in pairs (40%)
Anything noteworthy about delivery mode, assessment (with justification).
Knowledge Representation and Reasoning (KRR) is at the core of Artificial
Intelligence. It is concerned with the representation of knowledge
in symbolic form and the use of this knowledge for reasoning.
This course presents current trends and research issues in Knowledge
Representation and Reasoning (KRR).
It enables students interested in Artificial Intelligence to deepen their
knowledge in this important area and gives them a solid background for
doing their own work/research in this area. The topics covered in more detail
are AI Logics, Probablilistic Reasoning, Constraints, and Game Theory. We
require that participants have completed at least one introductory AI course
(COMP3411, COMP4415, COMP9414).
References (per topic):
1. Logic/ 2. AI Logics:
(a) Ronald J. Brachman and Hector J. Levesque.
Knowledge Representation and Reasoning,
Morgan Kaufmann, 2004.
(b) Michael R. Genesereth and Nils J. Nilsson.
Logical Foundations of Artificial Intelligence,
Morgan Kaufmann, 1987.
3. Probabilistic reasoning:
(a) Judea Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of
Plausible Inference, Morgan Kaufmann, 1988.
(b) Stuart Russell and Peter Norvig,
Artificial Intelligence: A Modern Approach,
Prentice Hall, 2nd Edition, 2003.(http://aima.cs.berkeley.edu/)
(a) Rina Dechter. Constraint Processing. Morgan Kaufmann, 2003.
(b) Grzegorz Kondrak and Peter van Beek. A theoretical evaluation of
selected backtracking algorithms. Artificial Intelligence, 89:365-387,
(c) A. G. Cohn and S. M. Hazarika. Qualitative Spatial Representation
and Reasoning: An Overview. Fundamenta Informaticae, 46 (1-2), pp 1-29,
(d) J. Renz, Qualitative Spatial Reasoning
with Topological Information, LNCS 2293, Springer,
5. Game theory:
(a) Selected lecture slides from the site
(b) Luce and Raiffa: Games and Decisions. Dover Publications.
Indicative syllabus / overview of contents (at a level of detail
well beyond that of the handbook entry)
- Logic Recap (1 week)
- AI Logics (3 weeks): Non-monotonic Reasoning, Default Reasoning, Belief
- Probablilistic Reasoning (3 weeks): Bayesian Nets, POMDP's,...
- Constraints (3 weeks): Constraint solving, CLP, Spatial and Temporal
- Game Theory (3 weeks): Introduction, ...
- Project presentations (1 week): course participants present their
projects. In addition, the Ph.D. students of our group will give a short
presentation of their Ph.D. projects
Effect on School Resources:
1. Who is proposed to teach the proposed new course,
and what impact would this have on their planned/current allocation?
The teaching will be shared by the four proposers in equal shares.
Dr. Meyer and Dr. Renz are NICTA employee. Prof. Foo and Dr. Pagnucco.
2. What sort of tutorial component is proposed, if any?
3. What is the likely impact on lab utilisation (this relates
to assignment and project work as well as scheduled labs?
Only for preparing assignments and the project. Very little impact if any.
4. Any other resource needs? E.g. special print/disk quota, access to
servers, access to special machines, special labs.