AI Game Programming Wisdom
AI Game Programming Wisdom
BDI agents for game development
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Introduction to Multiagent Systems
Introduction to Multiagent Systems
First Draft of a Report on the EDVAC
IEEE Annals of the History of Computing
Review: Intelligent Agents for Computer Games
CG '00 Revised Papers from the Second International Conference on Computers and Games
Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents
ECAI '96 Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Machine learning techniques for FPS in Q3
Proceedings of the 2004 ACM SIGCHI International Conference on Advances in computer entertainment technology
Mainstream Games in the Multi-agent Classroom
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Algorithmic Game Theory
A survey on the need and use of AI in game agents
Proceedings of the 2008 Spring simulation multiconference
Game-theoretic recommendations: some progress in an uphill battle
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Approximating mixed Nash equilibria using smooth fictitious play in simultaneous auctions
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Designing BOTs with BDI agents
CTS '09 Proceedings of the 2009 International Symposium on Collaborative Technologies and Systems
The RoboCup synthetic agent challenge 97
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
The symmetric Banzhaf value for fuzzy games with a coalition structure
International Journal of Automation and Computing
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In modern computer games, "bots" -- intelligent realistic agents play a prominent role in the popularity of a game in the market. Typically, bots are modeled using finite-state machine and then programmed via simple conditional statements which are hard-coded in bots logic. Since these bots have become quite predictable to an experienced games' player, a player might lose interest in the game. We propose the use of a game theoretic based learning rule called fictitious play for improving behavior of these computer game bots which will make them less predictable and hence, more a enjoyable game.