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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)
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)
Fast Downward Stone Soup is a sequential portfolio planner that uses various heuristics and search algorithms that have been implemented in the Fast Downward planning system. We present a simple general method for concocting “planner soups”, sequential portfolios of planning algorithms, and describe the actual recipes used for Fast Downward Stone Soup in(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)
Action pruning is one of the most basic techniques for improving a planner’s performance. The challenge of preserving optimality 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)
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 heuristic 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)
As our world becomes better connected and autonomous agents no longer appear to be science fiction, a natural need arises for enabling groups of selfish agents to cooperate in generating plans for diverse tasks that none of them can perform alone in a cost-effective manner. While most work on planning for/by selfish agents revolves around finding stable(More)
Merge-and-shrink abstraction is a general approach to heuristic design whose key advantage is its capability to make very fine-grained choices in defining abstractions. The Mergeand-shrink planner uses two different strategies for making these choices, both based on the well-known notion of bisimulation. The resulting heuristics are used in two sequential(More)