Systems That Know What They're Doing
IEEE Intelligent Systems
Optimized execution of action chains using learned performance models of abstract actions
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Camera-based observation of football games for analyzing multi-agent activities
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
GrAM: reasoning with grounded action models by combining knowledge representation and data mining
Proceedings of the 2006 international conference on Towards affordance-based robot control
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As agent systems are solving more and more complex tasks in increasingly challenging domains, the systems themselves are becoming more complex too, often compromising their adaptivity and robustness. A promising approach to solve this problem is to provide agents with reflective capabilities. Agents that can reflect on the effects and expected performance of their actions, are more aware and knowledgeable of their capabilities and shortcomings.In this paper, we introduce a computational model for what we call action awareness. To achieve this awareness, agents learn predictive action models from observed experience. This knowledge is then used to optimize, transform and coordinate plans. We apply this computational model to a number of typical scenarios from robotic soccer. Various experiments on real robots demonstrate that action awareness enables the robots to improve the performance of their plans substantially.