by M Beetz and H Grosskreutz
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.
Reference:
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior (M Beetz and H Grosskreutz), In Proceedings of the Sixth International Conference on AI Planning Systems, AAAI Press, 2000.
Bibtex Entry:
@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.},
}