Course Information Undergraduate prospectus

Artificial Intelligence

Course summary

Course code: COMP1694
Level: 6
Credits: 15
School: Architecture, Computing and Hums
Department: Computing and Information Sys.
Course Coordinator(s): Jixin Ma

Specification

Pre and co requisites

Logical Foundations, preferred but not essential.

Aims

The aim of this course is to provide knowledge and skills in the use of the concepts, techniques and tools of Artificial Intelligence, in order to enhance the students' appreciation/understanding of:
1. knowledge representation (Logic, Procedural, Network and Structured) and reasoning;
2. the logic-based programming language, PROLOG;
3. deterministic, graph search techniques and their implementation;
4. some advanced AI reasoning disciplines;
5. some techniques underpinning AI applications such as Games, etc.



Learning outcomes

On completing this course successfully the students are expected to be able to:
A. Use logic as a representation and reasoning strategy for AI;
B. Critically describe and discuss representation schemas such as Procedural Representations, Network Representations and Structured Representations and apply these to case studies.
C. Be proficient in the use of the PROLOG programming language;
D. Critically select and apply a variety of techniques underpinning AI applications such as Games, Temporal/Spatial Reasoning, etc.

Indicative content

Predicate logic - language, interpretation, inference, horn clauses and unification.
Symbolic programming using PROLOG with applications to search and planning.
The induction of logical conjunctions, decision trees, neural networks and sets of rules.
Advanced reasoning techniques such as: Bayesian reasoning, temporal logic and spatial reasoning
Selected applications such as Games, etc.

Teaching and learning activity

Concepts and techniques will be introduced in lectures and problem solving will be done through tutorials. Practical work will be through laboratory sessions.

Student time will be:
Lecture 50%;
Tutorial 25%;
Laboratory 25%.

Learning Time (1 credit = 10 hours).

Scheduled contact hours:

Note: include in scheduled time: project supervision, demonstrations, practical classes and workshops, supervised time in studio or workshop, scheduled lab work , fieldwork, external visits, work-based learning where integrated into a structured academic programme.
Lectures 24;
seminars 0;
supervised practical sessions 0;
Tutorials 12;
formative assessment 0;
other scheduled time 0.
Guided independent study:

Note: include in guided independent study preparation for scheduled sessions, follow up work, wider reading or practice, revision.
Independent coursework 50;
Independent laboratory work 48;
other non-scheduled time 16;
Placements (including work placement and year abroad) 0.
Total hours (Should be equal to credit x 10) 150.

Assessment

Methods of Assessment:

Coursework; grading mode - numeric; weighting% - 100; pass mark - 40; word length - 5000, outline details - develop and evaluate an AI application; last item of assessment - coursework; are students required to pass all components in order to pass the course - yes.