Grounding inference in distributed multi-robot environments

Systems embedded in a dynamic environment face the problem of grounding the inference used in their reasoning system to actual physical objects. 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 the environment. While some progress has been made grounding inference in tiered architectures for the single robot case, physical multi-robot systems invariably utilize purely behavior-based control techniques. We believe this is due to the complexities of synchronizing multiple distributed knowledge databases located on wireless platforms in real time. In this paper, we describe an inference grounding and coordination mechanism for small cooperative robot teams based on an extension of tagged behavior-based systems. Tagged behavior-based systems support a large subset of classical AI architectures while allowing object representations to remain distributed across multiple sensory and representational modalities. They provide a novel representation based on bit-vectors that allow team members to share intentional, attentional and sensory information using relatively low-bandwidth connections. We illustrate our approach on two problems involving systematic spatial search