OCOM5201M Knowledge Representation and Reasoning

The module provides a grounding in the techniques of Knowledge Representation and Reasoning and how they are used in the wider field of Artificial Intelligence. General concepts of this approach are explained, and a range of specific logical representations are introduced for representing different types of information (e.g. temporal and spatial information). Students will learn how to use these representations to encode a variety of real-world problems and how logical inference can be used to solve them. They will also learn how to use software tools to carry out automated reasoning.

Course Description

Syllabus

Indicative content for this module includes:

Review of logical foundations of knowledge representation including key properties of formal systems (such as soundness, completeness, expressiveness and tractability). Principles of Logic Programming.


Representing and reasoning about time and actions and physical changes (e.g., interval calculus, event calculus). Representing space and physical situations (topology, orientation, physical objects). Automated inference techniques (e.g., refinements of resolution, relational composition, non-monotonic reasoning). Ontology representation languages and tools. Semantic web applications.


Formalisms for representing other aspects of knowledge (e.g., vagueness, uncertainty, belief, desire).

 

Learning outcomes
On completion of the module students should be able to:

1. Analyse informal descriptions of moderately complex real world scenarios in terms of a number of different formal representation languages;

2. Use an automated reasoning software tool to compute inferences from logical representations;

3. Describe the principles of automated reasoning and the power limitations of different representations and inference mechanisms;

4. Create a simple ontology and use it within an information system.