Module Title:Artificial Intelligence for Games
Language of Instruction:English
Credits: 10
NFQ Level:8
Module Delivered In 1 programme(s)
Teaching & Learning Strategies: As well as traditional lectures students will undertake various laboratory exercises implementing various algorithms. They will be expected to participate in class on the materials covered. A term paper will involve a more in-depth study of the issues raised.
Module Aim: To introduce the formal theory behind, the current techniques in, and the application of Artificial Intelligence in Games.
Learning Outcomes
On successful completion of this module the learner should be able to:
LO1 Demonstrate a familiarity with the logical foundations of symbolic AI
LO2 Demonstrate a familiarity with non symbolic approaches to AI
LO3 Compare and contrast a number of adversarial search techniques
LO4 Illustrate different techniques for modelling/implementing the Game space
LO5 Apply appropriate AI techniques to solve various Gaming problems
Pre-requisite learning
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.

No recommendations listed
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed.
No requirements listed
 

Module Content & Assessment

Indicative Content
What is Intelligence?
Turing Test. Chinese Room. Philosophical Implications, AI in Games Context.
Basic Behaviours
Flocking, Swarming, Chasing, Evading.
Group Behaviours
Flocking, Swarming, Coordinated movements, Squads
Search
Search space, Basic search algorithms, Heurisitc Search, A* Search, Advanced A* variants
Game Search
Mini-max search, alpha-beta search, search space pruning
Basic Decision Making
Finite State Machines, Decision Trees
Fuzzy Logic
Fuzzification, Fuzzy Rule Application, Defuzzification, Combs Method
Probability
Basic Probability, Bayes rule, Bayesian Reasoning (Networks)
Artificial Neural Networks
Perceptron, Multilayer Networks, Backpropagation, Hopfield Networks, Simmulated Annealing
Genetic Algorithms
Genetic encoding, Genetic Operators, Selection
Agent based AI.
BDI Architecture. Subsumption Architecture
Assessment Breakdown%
Continuous Assessment40.00%
End of Module Formal Examination60.00%
Continuous Assessment
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Case Studies n/a 1,2,3,4,5 25.00 n/a
Practical/Skills Evaluation n/a 1,2,3,4,5 15.00 n/a
No Project
No Practical
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam n/a 1,2,3,4,5 60.00 End-of-Semester

ITCarlow reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Frequency Average Weekly Learner Workload
Lecture 30 Weeks per Stage 2.00
Laboratory 30 Weeks per Stage 2.00
Estimated Learner Hours 30 Weeks per Stage 3.20
Total Hours 216.00
 

Module Delivered In

Programme Code Programme Semester Delivery
CW_KCCGD_B Bachelor of Science (Honours) in Computer Games Development 4 Mandatory