Loïg Jezequel
Abstract
Factored planning mitigates the state explosion problem by avoiding the construction of the state space of the whole system and instead working with the system's components. Traditionally, finite automata have been used to represent the components, with the overall system being represented as their product. In this article, we change the representation of components to safe Petri nets. This allows one to use cheap structural operations like transition contractions to reduce the size of the Petri net before its state space is generated, which often leads to substantial savings compared with automata. The proposed approach has been implemented and proved efficient on several factored planning benchmarks. This article is an extended version of our ACSD 2013 paper [Jezequel et al. 2013], with the addition of the proofs and the experimental results of Sections 6 and 7.
- Eyal Amir and Barbara Engelhardt. 2003. Factored planning. In Proceedings of the 18th International Joint Conference on Artificial Intelligence. 929--935. Google Scholar
Digital Library
- Charles André. 1982. Structural transformations giving b-equivalent PT-nets. In Proceedings of the 3rd European Workshop on Applications and Theory of Petri Nets. 14--28. Google ScholarDigital Library
- Avrim Blum and Merrick Furst. 1995. Fast planning through planning graph analysis. Artificial Intelligence 90, 1--2 (1995), 281--300. Google ScholarDigital Library
- Hans Bodlaender. 1993. A linear time algorithm for finding tree-decompositions of small treewidth. In Proceedings of the 25th Annual ACM Symposium on Theory of Computing. 226--234. Google ScholarDigital Library
- Blai Bonet, Patrik Haslum, Sarah Hickmott, and Sylvie Thiébaux. 2008. Directed unfolding of Petri nets. Transactions on Petri Nets and Other Models of Concurrency 1, 1 (2008), 172--198. Google ScholarDigital Library
- Ronen Brafman and Carmel Domshlak. 2008. From one to many: Planning for loosely coupled multi-agent systems. In Proceedings of the 18th International Conference on Automated Planning and Scheduling. 28--35.Google Scholar
- Tam-Anh Chu. 1987. Synthesis of Self-Timed VLSI Circuits from Graph-Theoretic Specifications. PhD thesis. Massachusetts Institute of Technology.Google Scholar
- James C. Corbett. 1996. Evaluating deadlock detection methods for concurrent software. IEEE Transactions on Software Engineering 22 (1996), 161--180. Google ScholarDigital Library
- Javier Esparza and Keijo Heljanko. 2008. Unfoldings -- A Partial-Order Approach to Model Checking. Springer. Google ScholarDigital Library
- Javier Esparza, Stefan Romer, and Walter Vogler. 1996. An improvement of McMillan's unfolding algorithm. Formal Methods in System Design 20, 3 (1996), 285--310. Google ScholarDigital Library
- Eric Fabre. 2007a. Bayesian Networks of Dynamic Systems. Habilitation à Diriger des Recherches. Université de Rennes.Google Scholar
- Eric Fabre. 2007b. Trellis processes: A compact representation for runs of concurent systems. Discrete Event Dynamic Systems 17, 3 (2007), 267--306. Google ScholarDigital Library
- Eric Fabre and Loïg Jezequel. 2009. Distributed optimal planning: An approach by weighted automata calculus. In Proceedings of the 48th IEEE Conference on Decision and Control. 211--216.Google ScholarCross Ref
- Eric Fabre, Loïg Jezequel, Patrik Haslum, and Sylvie Thiébaux. 2010. Cost-optimal factored planning: Promises and pitfalls. In Proceedings of the 20th International Conference on Automated Planning and Scheduling. 65--72.Google Scholar
- Peter Hart, Nils Nilsson, and Bertram Raphael. 1968. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics 4, 2 (1968), 100--107.Google ScholarCross Ref
- Sarah Hickmott, Jussi Rintanen, Sylvie Thiébaux, and Lang White. 2007. Planning via Petri net unfolding. In Proceedings of the 19th International Joint Conference on Artificial Intelligence. 1904--1911. Google ScholarDigital Library
- Loïg Jezequel. 2012. Distributed Cost-Optimal Planning. PhD dissertation. ENS Cachan.Google Scholar
- Loïg Jezequel, Eric Fabre, and Victor Khomenko. 2013. Factored planning: From automata to petri nets. In Proceedings of the 13th International Conference on Application of Concurrency to System Design. 137--146. Google ScholarDigital Library
- Victor Khomenko, Alex Kondratyev, Maciej Koutny, and Walter Vogler. 2006. Merged processes: A new condensed representation of petri net behaviour. Acta Informatica 43, 5 (2006), 307--330. Google ScholarDigital Library
- Victor Khomenko, Mark Schaefer, and Walter Vogler. 2008. Output-determinacy and asynchronous circuit synthesis. Fundamenta Informaticae 88, 4 (2008), 541--579. Google ScholarDigital Library
- Victor Khomenko, Mark Schaefer, Walter Vogler, and Ralf Wollowski. 2009. STG decomposition strategies in combination with unfolding. Acta Informatica 46, 6 (2009), 433--474. Google ScholarDigital Library
- Walter Vogler and Ben Kangsah. 2007. Improved decomposition of signal transition graphs. Fundamenta Informaticae 78, 1 (2007), 161--197. Google ScholarDigital Library
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