Grounding inference in distributed multi-robot environments

Abstract

Traditional symbolic reasoning systems are typically built on a transaction model of computation, which complicates the process of synchronizing their world models with changes in a dynamic environment. This problem is exacerbated in the multi-robot case, where there are now n world models keep in synch. In this paper, we describe an inference grounding and coordination mechanism for robot teams based on tagged behavior-based systems. This approach supports a large subset of classical AI techniques while providing a novel representation that allows team members to share information efficiently. We illustrate our approach on two problems involving systematic spatial search.