COEN 196/296 Artificial Intelligence for Gaming
Syllabus Projects Lecture Notes Instructor

Artificial Intelligence for Gaming

Course objectives: Students will learn how to design, program, and analyze artificial intelligence methods appropriate to a game’s design and have fun doing so.

Prerequisite: Programming capability in a language / platform that allows simple graphic animations (see below). Python tkinter is the recommended choice for its reasonable quality with limited programming effort, but serious gamers might want to use a graphics package such as Open GL. The instructor will provide a basic GUI animation.

Expected learning outcomes:
• Understanding of the issues and role of AI in the design of games
• Capability of programming autonomous movement of avatars
• Capability of designing and using path planning
• Capability of designing and implementing decision making and coordinating action based on finite states, fuzzy sets, Markov sets, or rules.
• Capability of understanding tactical and strategic AI.

Class assignment: Homework assignments using programming in Python, Lua/C, Visual C++, Visual C# forms,, Java or any programming platform that allows for simple animation (of the bouncing ball type) according to the student’s choice. Coding base will be provided in Python only.

Text Book: Artificial Intelligence for Games, Second Edition by Ian Millington and John Funge, Morgan-Kaufman (required)

Grade calculation: 20% group quizes, 10% individual quizes, 20% project, 20% midterms, 30% final examination

Tentative Schedule:

Week Topic
1 Introduction, Nature of Game AI, Game AI Design, Analytical Geometry 1
2 Simple State Machines, Computational Geometry, Kinetic and Dynamic Movement, Analytical Geometry 2
3 Steering and combining steering
4 Interaction with Physics engine, Jumping, Coordinated movement, Motor Control
5 Path finding methods
6 Decision Making: Decision trees, State Machines, Fuzzy Logic
7 Decision Making: , Markov Systems, Goal-oriented behavior, Rule-based systems, blackboard architectures
8 Decision Making, Tactics
9 Learning, Execution Management
10 Presentation and Evaluation of homework assignments
11 Final Examinations Week

2016 Thomas Schwarz, S.J., COEN, SCU SCU COEN AI for Games T. Schwarz
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