Privacy-Preserving Algorithm for Decoupling of Multi-Agent Plans with Uncertainty

The execution of multi-agent plans often requires communi- cation between agents in order to synchronize their tasks. In cases where communication is unreliable or undesirable, tem- poral decoupling algorithms allow agents to find a distributed execution strategy beforehand without requiring perfect com- munication on the fly. The state-of-the-art Multi-Agent Simple Temporal Network with Uncertainty (MaSTNU) frame- work extends the decoupling problem for Multi-Agent Simple Temporal Network (MaSTN) to allow the modeling of uncertain durations and allow agents to communicate when certain events occur and communication is available. How- ever, the existing approach assumes centralized knowledge of the MaSTNU, whereas in the multi-agent context, privacy is an important concern. In this paper, we propose a distributed, privacy-preserving algorithm for finding distributed execu- tion strategies for MaSTNU. Experiments also showed significant speed-up of the proposed algorithm when the multi- agent plan is loosely coupled and mostly private.