Privacy-concerned multiagent planning

Coordinated sequential decision making of a team of cooperative agents can be described by principles of multiagent planning. Provided that the mechanics of the environment the agents act in is described as a deterministic transitions system, an appropriate planning model is MA-Strips. Multiagent planning modeled as MA-Strips prescribes exactly what information has to be kept private and which information can be communicated in order to coordinate toward shared or individual goals. We propose a multiagent planning approach which combines compilation for a classical state-of-the-art planner together with a compact representation of local plans in the form of finite-state machines. Proving soundness and completeness of the approach, the planner efficiency is further boosted up using distributed delete-relaxation heuristics and using an approximative local plan analysis. We experimentally evaluate applicability of our approach in full privacy setting where only public information can be communicated. We analyze properties of standard multiagent benchmarks from the perspective of classification of private and public information. We show that our approach can be used with different privacy settings and that it outperforms state-of-the-art planners designed directly for particular privacy classification.