EE3-16 Artificial Intelligence
Lecturer(s): Dr Jeremy PittAims:
The aims of the course are to: (1) introduce efficient formulation of a problem space, a declarative notation for describing it, and algorithms for searching it; (2) introduce aspects of knowledge representation, propositional and modla logics, and algorithms for automated reasoning; and (3) introduce the concepts of intelligent agents and multi-agent systems for the design of complex, distributed, embedded systems.
Learning Outcomes:
By attending the course, students should be able to understand and apply algorithms for problem-solving search, understand and apply algorithms for automated reasoning, and how planning and reasoning can be combined in an intelligent agent. Such Agents can then be used as autonomous components in the design of multi-agent systems for distributed computing applications.
Syllabus:
Search: search space, problem formulation, generic graph search algorithm, graph theory; uninformed search strategies - depth first, breadth first, uniform cost, iterative deepening; informed search strategies - best first, A*, interative deepending A*; analysis of algorithms - completeness, complexity, optimality; minimax, alpha-beta search; reinforcement learning and potential fields for path planning. Knowledge representation and reasoning: knowledge acquisition, knowledge engineering; propositional logic - semantic proof, syntactic proof, soundness and completeness of proof systems; automated reasoning with KE; predictate calculus and expert systems; modal logic and practical reasoning. Distributed AI: agents, agency and multi-agent systems; BDI agents.
Assessment:
100% on exam in early spring term
Coursework contribution: 80%
Term: Autumn
Closed or Open Book (end of year exam): Closed
Coursework Requirement
Assessed problem sheets
Oral Exam Required (as final assessment): no
Prerequisite: None required
Course Homepage: http://www.iis.ee.ic.ac.uk/~j.pitt/Teaching.html
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