Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A combined tactical and strategic hierarchical learning framework in multi-agent games
Sandbox '08 Proceedings of the 2008 ACM SIGGRAPH symposium on Video games
Player modeling impact on player's entertainment in computer games
UM'05 Proceedings of the 10th international conference on User Modeling
International Journal of Web-Based Learning and Teaching Technologies
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This paper proposes a novel agent personality representation model used to provide interagent adaptation in modern games, coined as the Tactical Agent Personality (TAP). The TAP represents the tactical footprints of a game agent using a weighted network of actions. Directly using the action probabilities to model an agent's personality, removes the time and effort required by experts to craft the model as well as eliminates the performance dependency on expert knowledge. The effectiveness, versatility, generality, scalability, and robustness claims of the TAP architecture and its variations are applied and evaluated across a variety of game scenarios, namely, First-person shooters (FPSs), real-time strategy (RTS) games, and role-playing games (RPG), where they are shown to exhibit plausible adaptive behavior.