COEN 129 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, Silverlight.net, 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
These documents are not intended for dissemination beyond SCU.        CAVEAT LECTOR