EE Department Intranet -
Close window CTRL+W

ELEC60004 Artificial Intelligence

Lecturer(s): Prof Jeremy Pitt


The overall aim of the course is to provide a foundational introduction to two essential aspects of Artifical Intelligence: planning and reasoning. The course focuses on algorithms for solving two types of question usually considered the domain of human intelligence and requiring some form of symbolic (rather than numerical or sub-symbolic) representation or reasoning: firstly: suppose I am at (or in situation) X, and want to get to Y: how do I get there? and secondly, suppose I know (or believe) that X is true, and want to know if Y is true, how do I do that?

The more specific aims of the course are to: (1) learn declarative specification and programming in Prolog; (2) introduce efficient formulation of a problem space, a declarative notation for describing it, and algorithms for searching it; (3) introduce aspects of knowledge representation using propositional, predicate and modal logics, and algorithms for automated reasoning with those logics; and (4) introduce the concept of temporal reasoning using the Event calculus.

Learning Outcomes

By attending the course, students should be able to write declarative programs in Prolog, be able to understand and apply algorithms for problem-solving search, and be able to understand and apply algorithms for automated reasoning in different logics.

At a higher level, students should also be able to see how to use:
(1) logic to do search, and search to do logic; and
(2) logic to do search to do logic.


Programming: declarative specification and programming in Prolog.
Planning: 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 for 2-player games; reinforcement learning and potential fields for path planning.
Reasoning: propositional logic, predicate logic, modal logic; semantic proof, syntactic proof, soundness and completeness of proof systems, knowledge representation and reasoning; automated reasoning using calculus KE (propositional and modal logic); temporal reasoning using the Event Calculus.
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

Course Homepage:

Book List: