A Rationale-Driven Team Plan Representation for Autonomous Intra-Robot Replanning

Abstract

For autonomous multi-robot teams, the individual team members are tasked with completing their assigned tasks as defined by a team plan provided by a centralized team planner. However in complex dynamic domains, the team plans are generated by the team planner with assumptions due to the complexity of modeling the domain. Failures in execution are therefore inevitable for the team members, and as such, replanning will occur for the team. In this paper, we introduce a rationale-driven team plan representation that provides rationales on why actions were chosen by the team planner. During a failure, the individual team members autonomously use our described intra-robot replanning algorithm to select all applicable replan policies for a given rationale. We then describe a method to learn the predicted cost of each replan policy, given a state of the environment, in order for the individual robots to select the lowest costing replan policy to improve team performance.