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ELEC70068 Artificial Intelligence

Lecturer(s): Prof Jeremy Pitt


The aims of the module are to introduce: (1) declarative specification and programming in Prolog; (2)planning: efficient formulation of a problem space as a graph, a declarative notation for representing it, a general engine and algorithms to search it to compute a plan; (3) aspects of knowledge representation and reasoning in propositional, predicate and modal logics, and algorithms for automated reasoning.

Learning Outcomes

Upon successful completion of this module, you will be able to: 1. use and evaluate different algorithms for searching a graph as a basis for planning and problem-solving 2. use and evaluate algorithms for automated reasoning in propositional, predicate and modal logics 3. apply formal languages for knowledge representation and reasoning through symbolic computation 4. write algorithms for planning and reasoning in Prolog (logic programming language)


Search: search space, problem formulation, general graph search algorithm, graph theory; uninformed search strategies - depth first, breadth first, uniform cost, iterative deepening; informed search strategies - best first, A*, iterative deepening A*; analysis of algorithms - completeness, complexity, optimality; 2-player games: minimax, minimax to fixed ply, alpha-beta search; reinforcement learning and potential fields for path planning. Knowledge representation and reasoning: knowledge acquisition, knowledge engineering; propositional logic, predicate loigc, modal logic; semantic proof, syntactic proof, soundness and completeness of proof systems; resolution, unification; automated reasoning with calculus KE in propositional, predicate and modal logic; Event Calculus and reasoning about actions and events.
Exam Duration: 3:00hrs
Coursework contribution: 0%

Term: Autumn

Closed or Open Book (end of year exam): Closed

Coursework Requirement:
         Assessed problem sheets

Oral Exam Required (as final assessment): no

Prerequisite module(s): None required

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Book List: