| CODE | ICS2206 | |||||||||
| TITLE | Knowledge Representation and Reasoning | |||||||||
| UM LEVEL | 02 - Years 2, 3 in Modular Undergraduate Course | |||||||||
| MQF LEVEL | 5 | |||||||||
| ECTS CREDITS | 5 | |||||||||
| DEPARTMENT | Artificial Intelligence | |||||||||
| DESCRIPTION | Knowledge Representation and Reasoning is a core area of study in Artificial Intelligence. Many problems that need an AI solution require knowledge to be represented, manipulated, and reasoned about. Knowledge may be critical to being able to derive a solution for many problems that require knowledge about the real world or a domain, and the choice of knowledge representation may drive the success or failure of the approach. Reasoning with knowledge is frequently necessary to derive new knowledge and/or deduce or infer facts. Students will have been exposed to preliminary approaches to knowledge, knowledge representation, and reasoning in the first year of studies - especially in Logic Programming, Foundations of Artificial Intelligence, and Mathematics of Discrete Structures 1. This study-unit first consolidates the material covered during the first year, and then introduces formal aspects of knowledge representation and reasoning, including a number of logic based formalisms for knowledge representations. In this study-unit, we investigate attempts to represent commonsense and scientific knowledge, and modelling concepts. The field of case-based reasoning - as a paradigm for combining problem-solving and learning, will also be introduced. In addition, the study-unit will include an introduction to the concept of Ontology, as a computational artefact that encodes knowledge about a domain in a machine-processable form to enable intelligent agents to manipulate and reason about this encoded knowledge." to make it more general. We will also briefly cover approaches to knowledge acquisition (to be covered in more detail in Knowledge Discovery and Management). Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: • Define and describe knowledge representation, its motivations, applicability advantages and pitfalls; • Describe and categorise some basic KR formalisms namely first-order and description logics; • Explain how automated reasoning can be used to help with modeling. 2. Skills: By the end of the study-unit the student will be able to: • Apply the basic range of techniques for building knowledge representations using standard tooling; • Analyse and compare different knowledge representations for specific tasks; • Design ontologies; • Evaluate knowledge representation techniques. Textbooks: • Ronald J. Brachman and Hector J. Levesque. Knowledge Representation and Reasoning, Morgan Kaufmann, 2004 • Franz Baader, Diego Calvanese, Deborah McGuinness, Daniele Nardi, Peter Patel-Schneider, The Description Logic Handbook: Theory, Implementation and Applications, Cambridge University Press, 2003 • Other selected papers and materials will be made available through the VLE |
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| RULES/CONDITIONS | Before TAKING THIS UNIT YOU MUST TAKE ICS1017 OR TAKE ICS1020 Before TAKING THIS UNIT YOU ARE ADVISED TO TAKE ICS1015 |
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| ADDITIONAL NOTES | Students taking this study-unit need to have a technical background. | |||||||||
| STUDY-UNIT TYPE | Lecture | |||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Joel Azzopardi (Co-ord.) Christopher D. Staff |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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