Reducing disclosed dependencies in privacy preserving planning

Rotem Lev Lehman, Guy Shani, Roni Stern

Research output: Contribution to journalArticlepeer-review


In collaborative privacy preserving planning (cppp), a group of agents jointly creates a plan to achieve a set of goals while preserving each others’ privacy. In state of the art cppp algorithms, the agents avoid explicitly sharing the value of private state variables. However, they may implicitly reveal dependencies between actions, that is, which action facilitates achieving the preconditions of another action. Previous work in cppp did not limit the disclosure of such dependencies. In this paper, we explicitly limit the amount of disclosed dependencies, allowing agents to publish only some of the dependencies between their actions. We investigate different strategies for deciding which dependencies to publish, and how they affect the ability to find solutions. We evaluate the ability of two solvers — distribute forward search and centralized planning based on a single-agent projection — to produce plans under this constraint. Experiments over standard cppp domains show that the proposed dependency-sharing strategies enable generating plans while sharing only a small fraction of all dependencies.

Original languageEnglish
Article number52
Number of pages1
JournalAutonomous Agents and Multi-Agent Systems
Issue number2
StatePublished - 10 Oct 2022


  • Privacy
  • Multi-agent
  • Planning

ASJC Scopus subject areas

  • Artificial Intelligence


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