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Module Descriptor School of Computer Science and Statistics

Module CodeCS4LL4
Module NameArtificial Intelligence IIa
Module Short TitleA I
Semester TaughtSemester 1 (Michaelmas)
Contact Hours43 hours (22 lecture, 10 lab, 11 tutorial)
Module PersonnelTim Fernando
Learning Outcomes

On successful completion of this module, students should be able to:
• Use description logics to express simple ontological constraints
• Apply finite-state methods to basic natural language processing tasks
• Evaluate the effectiveness of different approaches to reasoning about change in simple domains
• Understand the computational possibilities opened up by automata-theoretic approaches to reasoning

Learning Aims

An in-depth initiation into some topics in AI

Module Content

Knowledge Representation, Description Logics,
Finite-state methods, Reasoning about change

Recommended Reading List

Knowledge Representation and Reasoning, by Ronald Brachman and Hector Levesque (Elsevier, 2004)
+ course notes to be handed out in class

Module PrerequisitesProgramming competence (e.g. CS 3011)
Assessment Details

Exam (90%) and coursework (10%)
2 hour examination

Module Website
Academic Year of Data2016