גיא שני

Collaborative privacy-preserving planning (CPPP) is a multiagent planning task in which agents need to achieve a common set of goals without revealing certain private information. In many CPPP algorithms the individual agents reason about a projection of the multi-agent problem onto a singleagent classical planning problem. For example, an agent can plan as if it controls the public actions of other agents, ignoring their unknown private preconditions and effects, and use the cost of this plan as a heuristic for the cost of the full, multiagent plan. Using such a projection, however, ignores some dependencies between agents' public actions. In particular, it does not contain dependencies between actions of other agents caused by their private facts. We propose a projection in which these private dependencies are maintained. The benefit of our dependency-preserving projection is demonstrated by using it to produce high level plans in a new privacy preserving planner that is able to solve more benchmark problems than any other state-of-the-art privacy preserving planner. This more informed projection does not explicitly share private information. In addition, we show that even if an adversary agent knows that an agent has some private objects of a given type (e.g., trucks), it cannot infer how many such private objects the agent controls. This introduces a novel strong form of privacy that is motivated by real-world requirements.