To provide an introduction to the topic of Artificial Intelligence (AI) through studying problem-solving, knowledge representation, planning, and learning in intelligent systems.
To provide a grounding in the AI programming language Prolog.

(LO1) At the end of this module, students should be able to: identify or describe the characteristics of intelligent agents and the environments that they can inhabit;

(LO2) identify, contrast and apply to simple examples the major search techniques that have been developed for problem-solving in AI;

(LO3) distinguish the characteristics, and advantages and disadvantages, of the major knowledge representation paradigms that have been used in AI, such as production rules, semantic networks, propositional logic and first-order logic;

(LO4) solve simple knowledge-based problems using the AI representations studied;

(LO5) identify or describe approaches used to solve planning problems in AI and apply these to simple examples;

(LO6) identify or describe the major approaches to learning in AI and apply these to simple examples;

(LO7) identify or describe some of the major applications of AI;

(LO8) understand and write Prolog code to solve simple knowledge-based problems.

(S1) Numeracy/computational skills - Problem solving

(S2) Numeracy/computational skills - Reason with numbers/mathematical concepts

Introduction (3 lectures): What is Artificial Intelligence? Characterisation of AI; historical overview; intelligent agents; agents’ environments; applications of AI; current state-of-the-art.

Problem-Solving Through Search (7 lectures): Problem formulation; uninformed search strategies; informed search strategies; search in complex environments; adversarial search.

Knowledge Representation (4 lectures): Characterisation plus advantages and disadvantages of rule-based systems, semantic networks, ontologies and logics.  Example applications of different knowledge representation schemes.

Logic (4 lectures): Reasoning in propositional and first-order logic.

Planning (3 lectures): Representing planning problems; classical planning approaches, including search, heuristics and satisfiability; planning in complex environments.

Learning (4 lectures): Different forms of learning; logic and learning; reinforcement learning.

Basics of Prolog (5 l ectures): Facts, rules and queries; recursion; lists; negation as failure.

Teaching Method 1 - Lecture
Description:
Attendance Recorded: Not yet decided
Notes: 3 per week during semester

Teaching Method 2 - Laboratory Work
Description:
Attendance Recorded: Not yet decided