  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. During planning, agents often reveal the private dependencies between
their public actions to other agents, that is, which public action facilitates
the preconditions of another public action. Previous work in CPPP does not
limit the disclosure of such dependencies. In this paper, we explicitly limit
the amount of disclosed dependencies, allowing agents to publish only a part of
their private dependencies. 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 private dependencies.

Multiagent Systems;Artificial Intelligence