CS364 : Artificial Intelligence

Computers have become commonplace in many areas of our lives and are able to accomplish many things that humans would find difficult, if not impossible, to do by their own unaided efforts. Whilst computers can perform many calculations in a very short time they generally do not possess the ability learn or to reason about novel situations or to process incomplete or uncertain data. This module aims to demonstrate a variety of techniques for capturing human knowledge and represent it in a computer in a way that enables the machine to reason over the data represented and mimic the human ability to deal with incomplete or uncertain data.

At the end of the module students should be able to: Describe methods for acquiring human knowledge. Evaluate which of the acquisition methods would be most appropriate in a given situation. Describe techniques for representing acquired knowledge in a way that facilitates automated reasoning over the knowledge. Evaluate which of the representation techniques would be most appropriate in a given situation. Demonstrate one technique by which a machine may deal with uncertainty engendered by the imprecision of general language. Explain how machines may ‘learn’ and thus improve performance through ‘experience’.

Topics to be covered over 10 weeks:

• Knowledge Acquisition: Informal Interviews, Focused Interviews, Structured Interviews, Think Aloud protocols, Literature Review.
• Knowledge Representation: Semantic Nets, Conceptual Dependency Grammar, Conceptual Graphs.
• Uncertainty Management.
• Fuzzy Logic and Fuzzy Systems.
• Machine Learning and Data mining.

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