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UNIT TITLE: Artificial Intelligence for Games
CREDIT POINTS: 20
FHEQ LEVEL: 6
UNIT DESIGNATION: Traditional
UNIT CODE: CGP606
ACADEMIC SCHOOL: Media
Technology
Delivering School: Media
Technology
Date validated: May 2013
Date last modified: N/A
Unit delivery model: CD
Max & Min Student No: N/A
Arts
and
Arts
and
TOTAL STUDENT WORKLOAD
Students are required to attend and participate in all the formal scheduled sessions for
the unit. Students are also expected to manage their directed learning and independent
study in support of the unit.
PRE-REQUISITES AND CO-REQUISITES: None
UNIT DESCRIPTION
This unit explores the role of Artificial Intelligence (A.I.) in delivering software solutions
to real world problems. Emphasis is on those subjects that have immediate application,
especially in the development of novel solutions within the games industry. The unit
provides the student with advanced understanding of the power of a range of alternative
solutions to difficult real world and simulated problems, as well as a variety of A.I.
techniques available to software engineers to implement them.
LEARNING OUTCOMES
On successful completion of the unit, students should be able to:
Cognitive Skills
C1
Appraise and justify the application of A.I. techniques to given problem domains.
Practical and Professional Skills
P1
Apply A.I. techniques to given problem domains.
P2
Reflect and report upon the application of A.I. techniques in regards to more
traditional software solutions.
Transferable and Key Skills
T1
Use logical thinking to inform problem-solving and design decisions.
AREAS OF STUDY
Techniques
Representation
Models of symbolic and connectionist systems.
Search
Blind and heuristic approaches. Search spaces. Decision trees.
Reasoning
Logic, Production Systems, Forward and Backward Reasoning, Expert Systems.
Uncertainty
Bayesian approaches, Fuzzy logic.
Machine Learning
Back-propagation, Genetic Algorithms.
Applications
Natural Language Processing
Syntax, Semantics and Ambiguity, Use of Metaphor.
Games
Pathfinding, Game Bots, Strategy.
Robotics
Control Systems, Industrial Robotics, Autonomous systems.
Computer Vision
Techniques, Applications.
LEARNING AND TEACHING STRATEGY
Lectures are supplemented with discussion seminars and laboratory sessions. Artificial
Intelligence techniques are presented in the lectures, and the focus of these sessions is to
engage the students in problem-solving using these A.I. techniques. The laboratory
sessions will concentrate on the implementation of A.I. solutions using both symbolic and
connectionist systems. The seminars will consider the application of these techniques to a
range of current technological problems.
ASSESSMENT STRATEGY
The summative assessments are in the form of an individual Project Output and an
individual Project Report.
Students will receive formative feedback in laboratory sessions which will give them
practical experience of the techniques required to successfully complete development of
various A.I. systems.
The individual Project Output (AE1) will develop and assess the student’s ability to
evaluate, critique and question the applicability of the use of A.I. techniques for solving a
given problem. This will involve development of A.I. technique(s) to solve a problem and
evaluating the solution in the light of empirical results.
The individual Project Report (AE2) will test the student’s ability to rationalise as well as
suggest and design a solution to a given scenario and the ability to articulate an
understanding of the range of available tools and techniques that are appropriate given
the context and background of the problem.
ASSESSMENT
AE1
weighting:
assessment type:
length/duration:
online submission:
grade marking:
anonymous marking:
AE2
weighting:
assessment type:
length/duration:
online submission:
grade marking:
anonymous marking:
50%
Project Output
1000 words
No
Yes
No
50%
Project Report
2000 words
No
Yes
No
Aggregation of marks
The marks for each element of assessment will be aggregated to give an overall mark for
the unit.
Re-assessment Arrangements
Students referred in the Project Output (AE1) will be given a new assessment specification
that requires the production of a new project output, which will involve development of
A.I. technique(s) to solve a problem and evaluating the solution in the light of empirical
results.
Students referred in the Project Report (AE2) will be given a new problem scenario, and
will be required to rationalise, suggest and design a solution to the problem by articulating
an understanding of the range of available tools and techniques that are appropriate given
the context and background of the problem.
Unit Authors: Dr Brian Dupée/ Dave Horne
Date of version: May 2013