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Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior (bibtex)
@ARTICLE{beetz_probabilistic_2005,
  author = {Beetz, Michael and Grosskreutz, Henrik},
  title = {Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven
	Robot Behavior},
  journal = {Journal of Artificial Intelligence Research},
  year = {2005},
  volume = {24},
  pages = {799–849},
  abstract = {This article develops Probabilistic Hybrid Action Models ({PHAMs)},
	a realistic causal model for predicting the behavior generated by
	modern percept-driven robot plans. {PHAMs} represent aspects of robot
	behavior that cannot be represented by most action models used in
	{AI} planning: the temporal structure of continuous control processes,
	their non-deterministic effects, several modes of their interferences,
	and the achievement of triggering conditions in closed-loop robot
	plans. The main contributions of this article are: (1) {PHAMs}, a
	model of concurrent percept-driven behavior, its formalization, and
	proofs that the model generates probably, qualitatively accurate
	predictions; and (2) a resource-efficient inference method for {PHAMs}
	based on sampling projections from probabilistic action models and
	state descriptions. We show how {PHAMs} can be applied to planning
	the course of action of an autonomous robot office courier based
	on analytical and experimental results.}
}
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Last edited 29.01.2013 17:37 by Quirin Lohr