Computer facial animation
The digital Michelangelo project: 3D scanning of large statues
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Efficient rendering and compression for full-parallax computer-generated holographic stereograms
Efficient rendering and compression for full-parallax computer-generated holographic stereograms
Lpics: a hybrid hardware-accelerated relighting engine for computer cinematography
ACM SIGGRAPH 2005 Papers
Automatic computer game balancing: a reinforcement learning approach
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
IEEE Transactions on Visualization and Computer Graphics
The case for dynamic difficulty adjustment in games
Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology
Real-time hierarchical POMDPs for autonomous robot navigation
Robotics and Autonomous Systems
Player modeling impact on player's entertainment in computer games
UM'05 Proceedings of the 10th international conference on User Modeling
International Journal of Computer Games Technology
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This paper presents a novel approach to modeling a generic cognitive framework in game agents to provide tactical behavior generation as well as strategic decision making in modern multi-agent computer games. The core of our framework consists of two characterization concepts we term as the tactical and strategic personalities, embedded in each game agent. Tactical actions and strategic plans are generated according to the weights defined in their respective personalities. The personalities are constantly improved as the game proceeds by a learning process based on reinforcement learning. Also, the strategies selected at each level of the agents' command hierarchy affect the personalities and hence the decisions of other agents. The learning system improves performance of the game agents in combat and is decoupled from the action selection mechanism to ensure speed. The variability in tactical behavior and decentralized strategic decision making improves realism and increases entertainment value. Our framework is implemented in a real game scenario as an experiment and shown to outperform various scripted opponent team tactics and strategies, as well as one with a randomly varying strategy.