| Module Title: | Artificial Intelligence for Games |
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| Language of Instruction: | English |
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| Module Delivered In |
No Programmes
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| 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 |
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What is Intelligence?
Turing Test. Chinese Room. Philosophical Implications, AI in Games Context.
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Basic Behaviours
Flocking, Swarming, Chasing, Evading.
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Group Behaviours
Flocking, Swarming, Coordinated movements, Squads
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Search
Search space, Basic search algorithms, Heurisitc Search, A* Search, Advanced A* variants
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Game Search
Mini-max search, alpha-beta search, search space pruning
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Basic Decision Making
Finite State Machines, Decision Trees
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Fuzzy Logic
Fuzzification, Fuzzy Rule Application, Defuzzification, Combs Method
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Probability
Basic Probability, Bayes rule, Bayesian Reasoning (Networks)
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Artificial Neural Networks
Perceptron, Multilayer Networks, Backpropagation, Hopfield Networks, Simmulated Annealing
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Genetic Algorithms
Genetic encoding, Genetic Operators, Selection
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Agent based AI.
BDI Architecture. Subsumption Architecture
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| Assessment Breakdown | % |
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| Continuous Assessment | 40.00% |
| End of Module Formal Examination | 60.00% |
| Continuous Assessment |
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| 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 |
| 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 |
SETU Carlow Campus 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 |
|
