A Privacy-preserving Model for the Multi-agent Propositional Planning Problem

@inproceedings{Bonisoli2014APM,
  title={A Privacy-preserving Model for the Multi-agent Propositional Planning Problem},
  author={Andrea Bonisoli and A. Gerevini and A. Saetti and I. Serina},
  booktitle={ECAI},
  year={2014}
}

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References

SHOWING 1-3 OF 3 REFERENCES
An approach to multi-agent planning with incomplete information
TLDR
This paper proposes a cooperative refinement planning approach, built upon the partial-order planning paradigm, that allows agents to work with incomplete information and to have incomplete views of the world, i.e. being ignorant of other agents' information and maintaining their own private information. Expand
Multi-agent A* for parallel and distributed systems
TLDR
This paper provides a simple formulation of multi-agent A*, with a parallel and distributed variant, which leads to super-linear speedup, solving benchmark problems that have not been solved before. Expand
From One to Many: Planning for Loosely Coupled Multi-Agent Systems
TLDR
This paper establishes an upper bound on the complexity of multi-agent planning problems that depends exponentially on two parameters quantifying the level of agents' coupling, and on these parameters only. Expand