Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Distributed Decoupling of Multiagent Simple Temporal Problems / 408
Jayanth Krishna Mogali, Stephen F. Smith, Zachary B. Rubinstein

We propose a new distributed algorithm for decoupling the Multiagent Simple Temporal Network (MaSTN) problem. The agents cooperatively decouple the MaSTN while simultaneously optimizing a sum of concave objectives local to each agent. Several schedule flexibility measures are applicable in this framework. We pose the MaSTN decoupling problem as a distributed convex optimization problem subject to constraints having a block angular structure; we adapt existing variants of Alternating Direction Method of Multiplier (ADMM) type methods to perform decoupling optimally. The resulting algorithm is an iterative procedure that is guaranteed to converge. Communication only takes place between agents with temporal inter-dependences and the information exchanged between them is carried out in a privacy preserving manner. We present experimental results for the proposed method on problems of varying sizes, and demonstrate its effectiveness in terms of solving quality and computational cost.