Diagnosing Multi-Agent STRIPS Plans

Abstract

The increasing use of multi-agent systems demands that many challenges be addressed. One such challenge is diagnosing failed multi-agent plan executions, sometimes in system setups where the different agents are not willing to disclose their private actions. One formalism for generating multi-agent plans is the well-known MA-STRIPS formalism. While there have been approaches for delivering as robust plans as possible, we focus on the plan execution stage. Specifically, we address the problem of diagnosing plans that failed their execution. We propose a Model-Based Diagnosis approach to solve this problem. Given an MA-STRIPS problem, a plan that solves it, and an observation that indicates execution failure, we define the MA-STRIPS diagnosis problem. We compile that problem into a boolean satisfiability problem (SAT) and then use an off-the-shelf SAT solver to obtain candidate diagnoses. We further expand this approach to address privacy by proposing a distributed algorithm that can find these same diagnoses in a decentralized manner. Additionally, we propose an enhancement to the distributed algorithm that uses information generated during the diagnosis process to provide significant speedups. We found that the improved algorithm runs more than 10 times faster than the basic decentralized version and, in one case, runs faster than the centralized algorithm.