Automated Derivation of Primitives for Movement Classification
Autonomous Robots
Human-Level AI's Killer Application: Interactive Computer Games
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Automated derivation of behavior vocabularies for autonomous humanoid motion
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Behavior bounding: toward effective comparisons of agents & humans
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Identifying MMORPG bots: a traffic analysis approach
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing applications in network intrusion detection systems
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
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Modern, commercial computer games rely primarily on AI techniques that were developed several decades ago, and until recently there has been little impetus to change this. Despite the fact that the computer-controlled agents in such games often possess abilities far in advance of the limits imposed on human participants, competent players are capable of easily beating their artificial opponents, suggesting that approaches based on the analysis and imitation of human play may produce superior agents, in terms of both performance and believability.In this article, we describe our work in imitating the observed goal-oriented behaviors of a human player, based on concepts from data analysis and reinforcement learning. Since even the most intelligent artificial agent will be quickly identified as such if it is observed to move in a robotic manner, we also seek to incorporate mechanisms that will result in believably human-like motion. We then present some illustrative examples, demonstrating the effectiveness of our model. Finally, we discuss future work in this field.