Author:
David Thomson
Affiliation:
University of Canterbury, New Zealand
Keyword(s):
Opponent modeling, Adaptive AI, Machine learning, Student modeling, Video games, Quake 3.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Model-Based Reasoning
;
Reactive AI
;
Soft Computing
;
Symbolic Systems
Abstract:
Video games are quickly becoming a significant part of society with a growing industry that employs a wide range of talent, from programmers to graphic artists. Video games are also becoming an interesting and useful testbed for Artificial Intelligence research. Complex, realistic environmental constraints, as well as performance considerations demand highly efficient AI techniques. At the same time, the AI component of a video game may define the ongoing commercial success, or failure, of a particular game or game engine. This research details an approach to opponent modeling in a first person shooter game, and evaluates proficiency gains facilitated by such a technique. Information about the user is recorded and used by the existing Artificial Intelligence component to select tactics for any given opponent. The evaluation results show that when computer characters use such modeling they are more effective than when they do not model their opponent.