Technical University of Munich

@inproceedings{beetz_probabilistic_2000,
 author = {M Beetz and H Grosskreutz},
 title = {Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven
	Robot Behavior},
 booktitle = {Proceedings of the Sixth International Conference on {AI} Planning
	Systems},
 year = {2000},
 publisher = {{AAAI} Press},
 abstract = {This paper develops Probabilistic Hybrid Action Models ({PHAMs)},
	a realistic causal model for predicting the behavior generated by
	modern concurrent 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, and several modes of
	their interferences. The main contributions of the paper 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 discuss how {PHAMs} can be applied to planning
	the course of action of an autonomous robot office courier based
	on analytical and experimental results.},
}