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AIPS
2000

Probabilistic Hybrid Action Models for Predicting Concurrent Percept-Driven Robot Behavior

13 years 11 months ago
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-Driven Robot Behavior
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.
Michael Beetz, Henrik Grosskreutz
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where AIPS
Authors Michael Beetz, Henrik Grosskreutz
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