306718 (v.1) Artificial Machine Intelligence 551

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Area:	Department of Computing
Credits:	25.0
Contact Hours:	3.0
Lecture:	1 x 2 Hours Weekly
Practical:	1 x 1 Hours Weekly
Prerequisite(s):	5396 (v.7) Software Technology 502 or any previous version
Syllabus:	Introduction to LISP, list processing, functional approach. Pattern matching, symbolic representation, LISP - local state, stream processing, function as arguments and results, printing, reading and file manipulation. Search methods, semantic nets and frames in LISP, natural language processing, simple grammar representation. Introduction to neural networks. Introduction to machine learning, constraint propagation, probabilistic reasoning, planning and problem solving paradigms.
Unit Outcomes:	On the completion of this unit, students should have a basic understanding of the field Artificial Intelligence, its main problems and approaches, be familiar with symbolic representations, search methods, first-order logics and their applications in reasoning and learning tasks as well as be familiar with numerical techniques in AI, including Probabilistic Networks, Neural Networks and their applications.
Text and references listed above are for your information only and current as of September 30, 2003. Please check with the unit coordinator for up-to-date information.
Unit References:	G.L. Steele (1990), Common Lisp the Language 2nd ed. Thinking Machines Inc. Digital Press ISBN 1-55558-041-6. H. Abelson, G.J. Sussman and J. Sussman, (1989), Structure and Interpretation of Computer Programs, Addison-Wesley. ISBN: 0-201-08319-1
Unit Texts:	S. Russell and P. Norvig, (1995), Artificial Intelligence - A Modern Approach. Prentice Hall ISBN: 0 13 360124 2. P. Winston, P. H, Kaus, H. HORN, H. LISP, 3rd ed. ISBN 0-201-08319-1
Unit Assessment Breakdown:	Assignments (2) 40%, Final Examination 60%. This is by grade/mark assessment.
Year Location Period Internal Area External Central External 2004 Bentley Campus Semester 1 Y