Imitation-based evolution of artificial game players
ACM SIGEVOlution
On the Usefulness of Interactive Computer Game Logs for Agent Modelling
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Learning drivers for TORCS through imitation using supervised methods
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Learning a context-aware weapon selection policy for unreal tournament III
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Computational & Mathematical Organization Theory
Hi-index | 0.00 |
Modern interactive computer games provide the ability to objectively record complex human behavior, offering a variety of interesting challenges to the pattern-recognition community. Such recordings often represent a multiplexing of long-term strategy, mid-term tactics and short-term reactions, in addition to the more low-level details of the player's movements. In this paper, we describe our work in the field of imitation learning; more specifically, we present a mature, Bayesian-based approach to the extraction of both the strategic behavior and movement patterns of a human player, and their use in realizing a cloned artificial agent. We then describe a set of experiments demonstrating the effectiveness of our model.