Department of Computer Science
Informatik 9
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85748 Garching
Germany
Tel: +49-89-289-17759
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Office:
Mail: michael.beetz@in.tum.de
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Michael Beetz is a professor for Computer Science at the Department of Informatics of the Technische Universität Muenchen and heads the Intelligent Autonomous Systems group. From 2006 to 2011, he was vice coordinator of the German national cluster of excellence CoTeSys (Cognition for Technical Systems) where he is also co-coordinator of the research area “Knowledge and Learning”.
Michael Beetz received his diploma degree in Computer Science with distinction from the University of Kaiserslautern. He received his MSc, MPhil, and PhD degrees from Yale University in 1993, 1994, and 1996 and his Venia Legendi from the University of Bonn in 2000. Michael Beetz was a member of the steering committee of the European network of excellence in AI planning (PLANET) and coordinating the research area ``robot planning''. He is associate editor of the AI Journal. His research interests include plan-based control of robotic agents, knowledge processing and representation for robots, integrated robot learning, and cognitive perception.
@InProceedings{Bee00Pro,
author = {Michael Beetz and Henrik 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",
bib2html_pubtype = {Refereed Conference Paper},
bib2html_rescat = {Plan-based Robot Control},
bib2html_groups = {IAS},
bib2html_funding = {ignore},
bib2html_keywords = {Robot, Planning},
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.}
}