Distributed Algorithms for Incrementally Maintaining Multiagent Simple Temporal Networks

Abstract

When multiple agents want to maintain temporal information, they can employ a Multiagent Simple Temporal Network (MaSTN). Recent work has shown that the constraints in a MaSTN can be efficiently propagated by enforcing partial path consistency (PPC) with a distributed algorithm. However, new temporal constraints may arise continually due to ongoing plan construction or execution, the decisions of other agents, and other exogenous events. For these new constraints, propagation is again required to re-establish PPC. Because the affected part of the network may be small, one typically wants to exploit the similarities between the new and previous version of the MaSTN. To this end, we propose two new distributed algorithms for incrementally maintaining PPC. The first is inspired by TriSTP, the seminal PPC algorithm for STNs; the second is a distributed version of IPPC, which represents the current state of the art for incrementally enforcing PPC in a centralized setting. The worst-case time performance of these algorithms is similar to their centralized counterparts. We empirically compare our distributed algorithms, analyzing their performance under various assumptions, and demonstrate significant speedup over their centralized counterparts.