Computer Science, Prifysgol Cymru Aberystwyth University of Wales

CS46010 (1995-96 session)
AI Problem Solvers

Brief Description

This course deals with the implementation of advanced software tools. It builds on C362(h)/ CS36210 giving practical examples in Lisp of common AI techniques and strategies. It will provide an understanding of some important contemporary AI techiques and their applications. At the end of the course, the student should be able to employ such techniques to practical problem-solving tasks.

Aims, Objectives, Syllabus, Booklist

Further Details

Number of lectures: 0
Number of seminars/tutorials: 14
Number of practicals: 5
Coordinator: Dr Fred Long
Other staff involved: Not yet known
Pre-requisites: C362(h)/ CS36210 or equivalent experience
Co-requisites: None
Incompatibilities: None
Assessment: Assessed coursework - 50%
Written exam - 50%
Timing: This module is offered only in Semester 1

Aims

The course will be run as a number of seminars and workshops and will be heavily based on the Norvig book.

Objectives

On successful completion of this module, students should:

be able to read and understand complex Lisp programs
have enough experience of Lisp to be able to build their own AI programs in Lisp
understand the terms and techniques used in AI problem solving
be able to build software tools using AI techniques

Syllabus

Overview of Common Lisp - 2: What makes Lisp different. Introduction to the main features of the language and the local Lisp environments.
GPS - the General Problem Solver - 2: Description, implementation, example applications, further developments of GPS.
Pattern matching and search - 3: Segment pattern matching; heuristic tree search; implementing AI search strategies; seaching graphs.
Constructing advanced software tools - 3: Examination of the construction of a variety of software tools including at least some of the following: Interactive interpreter tools; pattern matching tools; rule-based translators; searching tools; theorem provers.
Efficiency issues - 2: Memoization; compiling one language into another; lazy evaluation; indexing data; low-level efficiency issues.