Computing Multi-Agent Heuristics Additively. Štolba, M. & Komenda, A. In
Computing Multi-Agent Heuristics Additively [link]Paper  abstract   bibtex   
Similarly to classical planning, heuristics play a crucial role in most multi-agent and privacy-preserving multi-agent planning systems. It has been shown that distributed heuristics may crucially improve the search guidance, but are costly in terms of communication and computation time and are often a source of privacy concerns. One solution is to compute a heuristic additively, in the sense that each agent can compute its part of the heuristic independently and obtain a complete heuristic estimate by summing up the individual parts. In this preliminary paper, we propose a technique based on cost-partitioning allowing us to use any heuristic in such a way.

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