Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior (bibtex)
by Michael Beetz, Henrik Grosskreutz
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.
Reference:
Michael Beetz, Henrik Grosskreutz, "Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior", In Journal of Artificial Intelligence Research, vol. 24, pp. 799-849, 2005.
Bibtex Entry:
@Article{beetz05probabilistic,
  author =	 {Michael Beetz and Henrik Grosskreutz},
  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},
  bib2html_pubtype  = {Journal},
  bib2html_rescat   = {},
  bib2html_groups   = {},
  bib2html_funding  = {},
  bib2html_keywords = {},
  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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