COMP 7021 Knowledge Representation and Reasoning

Credit Points 10

Legacy Code 301315

Coordinator Yan Zhang Opens in new window

Description Knowledge representation and reasoning is one of the fundamental components of Artificial Intelligence. It studies ways to represent and reason about human knowledge effectively in formal computational models, and eventually to solve complex tasks using computer systems. This unit covers logic foundations of knowledge representation, Answer Set Programming approaches for declarative problem solving, intelligent agent modelling, and theory and practice of knowledge graphs.

School Computer, Data & Math Sciences

Student Contribution Band HECS Band 2 10cp

Check your HECS Band contribution amount via the Fees page.

Level Postgraduate Coursework Level 7 subject

Learning Outcomes

On successful completion of this subject, students should be able to:
  1. Analyse the logic foundations of knowledge representation and reasoning in Artificial Intelligence
  2. Apply knowledge of the essentials of non-monotonic reasoning and applications of knowledge graphs
  3. Implement Answer Set Programming as a declarative programming language and its applications in general problem solving and in modelling dynamic domains
  4. Use formal languages based on Answer Set Programming to represent planning and diagnostic agents
  5. Communicate in a professional manner using the language of knowledge representation to diverse audiences

Subject Content

Logical Foundations for Knowledge Representation and Reasoning
Knowledge Representation and Non-monotonic Reasoning
Answer Set Programming: Syntax and Semantics
Declarative Problem Solving Using Answer Set Programming
Algorithms for Computing Answer Sets
Modelling Dynamic Domains
Planning and Diagnostic Agents
Graph Based Knowledge Representation
Knowledge Graph Applications

Assessment

The following table summarises the standard assessment tasks for this subject. Please note this is a guide only. Assessment tasks are regularly updated, where there is a difference your Learning Guide takes precedence.

Item Length Percent Threshold Individual/Group Task
Quiz 1 hour (per Quiz) 40 N Individual
Practical 2 hours 30 N Individual
Report 1000 words 30 N Individual

Prescribed Texts

  • Gelfond, M., & Kahl, Y. (2014). Knowledge representation, reasoning, and the design of intelligent agents. New York, NY: Cambridge University Press.

Teaching Periods