Multi-agent planning using MA-STRIPS–related models is often motivated by the preservation of private information. Such a motivation is not only natural for multi-agent systems but also is one of the main reasons multi-agent planning problems cannot be solved with a centralized approach. Although the motivation is common in the literature, the formal treatment of privacy is often missing. In this article, we expand on a privacy measure based on information leakage introduced in previous work, where the leaked information is measured in terms of transition systems represented by the public part of the problem with regard to the information obtained during the planning process. Moreover, we present a general approach to computing privacy leakage of search-based multi-agent planners by utilizing search-tree reconstruction and classification of leaked superfluous information about the applicability of actions. Finally, we present an analysis of the privacy leakage of two well-known algorithms—multi-agent forward search (MAFS) and Secure-MAFS—both in general and on a particular example. The results of the analysis show that Secure-MAFS leaks less information than MAFS.

As a part of the workshop on Distributed and Multiagent Planning (DMAP) at the International Conference on Automated Planning and Scheduling (ICAPS) 2015, we have organized a competition in distributed and multiagent planning. The main aims of the competition were to consolidate the planners in terms of input format; to promote development of multiagent planners both inside and outside of the multiagent research community; and to provide a proof-of-concept of a potential future multiagent planning track of the International Planning Competition (IPC). In this paper we summarize course and highlights of the competition.

Multi-agent planning (MAP) is often motivated by the preservation of private information. Such motivation is not only natural for multi-agent systems, but is one of the main reasons, why MAP problems cannot be solved centrally.