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Distributed Heuristic Forward Search for Multiagent Planning
"... This paper deals with the problem of classical planning for multiple cooperative agents who have private information about their local state and capabilities they do not want to reveal. Two main approaches have recently been proposed to solve this type of problem – one is based on reduction to dist ..."
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This paper deals with the problem of classical planning for multiple cooperative agents who have private information about their local state and capabilities they do not want to reveal. Two main approaches have recently been proposed to solve this type of problem – one is based on reduction to distributed constraint satisfaction, and the other on partialorder planning techniques. In classical singleagent planning, constraintbased and partialorder planning techniques are currently dominated by heuristic forward search. The question arises whether it is possible to formulate a distributed heuristic forward search algorithm for privacypreserving classical multiagent planning. Our work provides a positive answer to this question in the form of a general approach to distributed statespace search in which each agent performs only the part of the state expansion relevant to it. The resulting algorithms are simple and efficient – outperforming previous algorithms by orders of magnitude – while offering similar flexibility to that of forwardsearch algorithms for singleagent planning. Furthermore, one particular variant of our general approach yields a distributed version of the a * algorithm that is the first costoptimal distributed algorithm for privacypreserving planning. 1.
Computation of summaries using net unfoldings
"... We study the following summarization problem: given a parallel composition A = A1 ‖... ‖ An of labelled transition systems communicating with the environment through a distinguished component Ai, efficiently compute a summary Si such that E ‖ A and E ‖ Si are traceequivalent for every environment E ..."
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We study the following summarization problem: given a parallel composition A = A1 ‖... ‖ An of labelled transition systems communicating with the environment through a distinguished component Ai, efficiently compute a summary Si such that E ‖ A and E ‖ Si are traceequivalent for every environment E. While Si can be computed using elementary automata theory, the resulting algorithm suffers from the stateexplosion problem. We present a new, simple but subtle algorithm based on net unfoldings, a partialorder semantics, give some experimental results using an implementation on top of Mole, and show that our algorithm can handle divergences and compute weighted summaries with minor modifications.
THEME Networks and TelecommunicationsTable of contents
"... perfOrmance aNalYsiS Of networkS ..."
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Proceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS 2010) CostOptimal Factored Planning: Promises and Pitfalls
"... Factored planning methods aim to exploit locality to efficiently solve large but “loosely coupled ” planning problems by computing solutions locally and propagating limited information between components. However, all factored planning methods presented so far work with representations that require ..."
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Factored planning methods aim to exploit locality to efficiently solve large but “loosely coupled ” planning problems by computing solutions locally and propagating limited information between components. However, all factored planning methods presented so far work with representations that require certain parameters to be bounded (e.g. number of coordination points between local plans considered); the satisfaction of those bounds by a given problem instance is difficult to establish apriori, and the influence of those parameters on the problem complexity is unclear. We present an instance of the factored planning framework using a representation of the (regular) sets of local plans by finite automata, which does not require any such bound. By substituting weighted automata, we can even do factored costoptimal planning. We test an implementation of the method on the few standard planning benchmarks that we have found to be amenable to factoring. We show that this method runs in polynomial time under conditions similar to those considered in previous work, but not only under those conditions. Thus, what constitutes an essential measure of “factorability ” remains obscure.
ProjectTeam Distribcom Distributed Models and Algorithms for the Management of Telecommunication Systems
"... c t i v it y e p o r t 2008 Table of contents ..."
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Factored Planning: From Automata to Petri Nets
"... Abstract—Factored planning mitigates the state space 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 repre ..."
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Abstract—Factored planning mitigates the state space 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 paper 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 proven efficient on several factored planning benchmarks. I.
MessagePassing Algorithms for the Verification of Distributed Protocols
"... Abstract. Messagepassing algorithms (MPAs) are an algorithmic paradigm for the following generic problem: given a system consisting of several interacting components, compute a new version of each component representing its behaviour inside the system. MPAs avoid computing the full state space by ..."
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Abstract. Messagepassing algorithms (MPAs) are an algorithmic paradigm for the following generic problem: given a system consisting of several interacting components, compute a new version of each component representing its behaviour inside the system. MPAs avoid computing the full state space by propagating messages along the edges of the system interaction graph. We present an MPA for verifying local properties of distributed protocols with a tree communication structure. We report on an implementation, and validate it by means of two case studies, including an analysis of the PGM protocol.
Networks of automata with read arcs: a tool for distributed planning
"... Abstract: A planning problem consists in driving a system from its current state to a set of target states. Problems are generally expressed by a collection of state variables, and actions that change the value of a subset of these variables. Such problems can be modeled as networks of automata, one ..."
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Abstract: A planning problem consists in driving a system from its current state to a set of target states. Problems are generally expressed by a collection of state variables, and actions that change the value of a subset of these variables. Such problems can be modeled as networks of automata, one per state variable, that partially synchronize on some actions. Distributed planning (or factored planning) consists in driving each of these automata to its goal state(s), while preserving the coherence of their interactions. Real planning problems, however, need to model actions that can only be performed in one component when another component is in a specific state. This paper proposes a mechanism to capture this phenomenon, under the form of automata with read arcs, reproducing what already exists for Petri nets. It is shown that a previous approach to distributed planning, based on automata computations, can be extended to this new setting.
Turbo Planning
"... Abstract: The complexity of planning problems comes from the size of the state graph of the systems, which suggests to consider factored (or distributed) solutions. We previously proposed a solution of this kind which revealed to be very efficient on problems where components have a sparse interacti ..."
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Abstract: The complexity of planning problems comes from the size of the state graph of the systems, which suggests to consider factored (or distributed) solutions. We previously proposed a solution of this kind which revealed to be very efficient on problems where components have a sparse interaction. This work explores a step further in this direction. The idea is to extend the celebrated turbo algorithms, extremely successful to decode largescale sparse error correcting codes. The paper proposes an adaptation of this technique to the setting of costoptimal factored planning, and illustrates its behavior on large randomly generated systems.