A framework for planning in continuous-time stochastic domains
Abstract
We propose a framework for policy generation in continuous-time
stochastic domains with concurrent actions and events of uncertain
duration. We make no assumptions regarding the complexity of the
domain dynamics, and our planning algorithm can be used to generate
policies for any discrete event system that can be simulated. We use
the continuous stochastic logic (CSL) as a formalism for expressing
temporally extended probabilistic goals and have developed a
probabilistic anytime algorithm for verifying plans in our framework.
We present an efficient procedure for comparing two plans that can be
used in a hill-climbing search for a goal-satisfying plan. Our
planning framework falls into the Generate, Test and Debug paradigm,
and we propose a transformational approach to plan generation. This
relies on effective analysis and debugging of unsatisfactory plans.
Discrete event systems are naturally modeled as generalized
semi-Markov processes (GSMPs). We adopt the GSMP as the basis for our
planning framework, and present preliminary work on a domain
independent approach to plan debugging that utilizes information from
the verification phase.
Sample citation
Håkan L. S. Younes,
David J. Musliner, and
Reid G. Simmons. 2003.
A framework for planning in continuous-time stochastic domains. In
Proceedings of the Thirteenth International Conference on Automated Planning and Scheduling, edited by Enrico Giunchiglia, Nicola Muscettola, and Dana S. Nau, 195–204, Trento, Italy. AAAI Press.
Full paper (10 pages, 39 references)
Copyright © 2003, American Association for Artificial Intelligence. All rights reserved.
Presentation (34 slides)