by M Beetz and H 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:
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior (M Beetz and H Grosskreutz), In Journal of Artificial Intelligence Research, volume 24, 2005.
Bibtex Entry:
@article{beetz_probabilistic_2005,
author = {M Beetz and H 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},
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.},
}