Towards focused plan monitoring: a technique and an application to mobile robots

Abstract

Until recently, techniques for AI plan generation relied on highly restrictive assumptions that were almost always violated in real-world environments; consequently, robot designers adopted reactive architectures and avoided AI planning techniques. Some recent research efforts have focused on obviating such assumptions by developing techniques that enable the generation and execution of plans in dynamic, uncertain environments. In this paper, we discuss one such technique, rationale-based monitoring, originally introduced by Veloso, Pollack, and Cox (1998), and describe our use of it in a simple mobile robot environment. We review the original approach, describe how it can be adapted for a causal-link planner, and provide experimental results demonstrating that it can lead to improved plans without consuming excessive overhead. We also describe our use of rationale-based monitoring in a mobile robot office-assistant project currently in progress.