Action Awareness -- Enabling Agents to Optimize, Transform, and Coordinate Plans (bibtex)
by Freek Stulp, Michael Beetz
Abstract:
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 \emphaction 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.
Reference:
Freek Stulp, Michael Beetz, "Action Awareness -- Enabling Agents to Optimize, Transform, and Coordinate Plans", In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2006.
Bibtex Entry:
@InProceedings{stulp06actionawareness,
  author =       {Freek Stulp  and Michael Beetz},
  title =        {Action Awareness -- Enabling Agents to Optimize, Transform, and Coordinate Plans},
  booktitle =    {Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
  year =         {2006},
  bib2html_pubtype  = {Refereed Conference Paper},
  bib2html_rescat   = {Models, Learning, Planning, Action},
  bib2html_groups   = {AGILO},
  bib2html_funding  = {AGILO},
  bib2html_keywords = {},
  abstract = {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 \emph{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.}
}
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