Module manager: Dr Shabbar Naqvi
Email: S.Naqvi@leeds.ac.uk
Taught: Semester 1 May to 30 June View Timetable
Year running 2024/25
None
| OCOM5100M | Programming for Data Science |
This module is not approved as an Elective
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.
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. It covers basic theoretical ideas as well as a range of specific representation languages and inference systems. It develops skills of translating informal problems statements in a precise logical representation and using software tools to carry out automated reasoning.
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.
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).
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| On-line Learning | 6 | 1 | 6 |
| Group learning | 6 | 2 | 12 |
| Independent online learning hours | 28 | ||
| Private study hours | 104 | ||
| Total Contact hours | 18 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Private study will include directed reading and exercises and self-directed research in support of learning activities, as well as in preparation for assessments.
Independent online learning involves non-facilitated directed learning. Students will work through bespoke interactive learning resources and activities in the VLE.
Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow self and peer assessment providing opportunities for formative feedback from peers and tutors.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Report | Ontology Development & Modal Logic | 60 |
| Computer Exercise | Online test, classical logic and proofs | 40 |
| Total percentage (Assessment Coursework) | 100 | |
This module will be reassessed by a 100% individual assessment.
The reading list is available from the Library website
Last updated: 19/09/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team