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We present a fully distributed multi-agent planning algorithm. Our methodology uses distributed constraint satisfaction to coordinate between agents, and local planning to ensure the consistency of these coordination points. To solve the distributed CSP efficiently, we must modify existing methods to take advantage of the structure of the underlying(More)
A * with admissible heuristics is a very successful approach to optimal planning. But how to derive such heuristics automatically? Merge-and-shrink abstraction (M&S) is a general approach to heuris-tic design whose key advantage is its capability to make very fine-grained choices in defining abstractions. However, little is known about how to actually make(More)
Many areas of computer science require answering questions about reachability in compactly described discrete transition systems. Answering such questions effectively requires techniques to be able to do so without building the entire system. In particular, heuristic search uses lower-bounding (“admissible”) heuristic functions to prune parts of(More)
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(More)
Action pruning is one of the most basic techniques for improving a planner's performance. The challenge of preserving op-timality while reducing the state space has been addressed by several methods in recent years. In this paper we describe two optimality preserving pruning methods: The first is a generalization of tunnel macros. The second, the main(More)
This paper describes a number of distributed forward search algorithms for solving multi-agent planning problems. We introduce a distributed formulation of non-optimal forward search, as well as an optimal version, mad-a*. Our algorithms exploit the structure of multi-agent problems to not only distribute the work efficiently among different agents, but(More)
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