Luger, G. F. and Stubblefield, W. A. (1998) Artificial Intelligence: Structures And Strategies For Complex Problem Solving, Addison Wesley Longman, Reading USA
Russell, S and Norvig, P. (1995) Artificial Intelligence: a Modern Approach, Prentice Hall, New Jersey, USA
Background reading in AI: Finlay, J and Dix, A. (1996) An Introduction to Artificial Intelligence, UCL Press, UK
Communications of the ACM, July 1994, vol. 37, No 7. Special issue on Intelligent Agents
Communications of the ACM, March 1994, Vol. 37, No. 3. Special issue on Artificial Intelligence
AI magazine, Summer 1997. Intelligent Systems on the Internet

None

Internet resources for the selected sites
Bulletin board
Library
Software systems: PROLOG, HUGIN, SNNS

The learning strategies range from a series of lectures, tutorials, seminars, bulletin board based activities and internet-based support material. Staff contact to outline and discuss the main concepts, techniques and issues of the above mentioned topics, and independent study where students are expected both, (i) to investigate these issues further by examining the proposed reading material and support material, and (ii) to apply the acquired knowledge to develop a small intelligent system as part of their coursework.
(1:n)3 (1:20)1

Coursework (max 2000 words) - 50% (Learning outcomes 1,2 and 3)
Exam - 50% (Learning outcomes 1 and 2)

Typical skills will include ability to compare and contrast different knowledge representations and reasoning strategies, analyse social and legal implications of intelligent systems, and practical experience in designing such systems.

This module introduces students to the field of Artificial Intelligence and gives an overview of intelligent systems. Artificial Intelligence (AI) is often defined as the "branch of computer science that is concerned with the automation of intelligent behavior" (Luger et al, 1998). AI is a big field, in this module we shall focus on three fundamental aspects: knowledge, reasoning and learning. The module begins with an overview of AI applications, and introduces students to the programming language PROLOG and current approaches for solving AI problems. We discuss the role of knowledge and heuristics and examine a number of symbolic knowledge representation formalisms such as logic, semantic nets, frames, objects, production rules. We then investigate current reasoning techniques such as logic, rule based reasoning, reasoning under uncertainty, Bayesian networks, fuzzy logic and case-based reasoning. We study the sub-symbolic or connectionist approach to learning and analyse their strength and weaknesses. Finally we examine some important application areas of AI which apply the above techniques, analyse their knowledge representation schemes and reasoning model, and discuss issues related to the social and legal implications of intelligent systems. Typical applications may include natural language processing, knowledge discovery, machine learning, or agent-based problem solving, depending on the expertise of the AI team.