Adaptation and Communication in Human-Robot Teaming to Handle Discrepancies in Agents' Beliefs about Plans

TL;DR

This paper introduces a formal framework using epistemic logic and Monte Carlo Tree Search to enable agents in human-robot teams to identify and resolve discrepancies in their beliefs about plans, improving teamwork in uncertain scenarios.

Contribution

It extends conditional doxastic logic to model nested beliefs and develops an online planning algorithm for communication and adaptation in non-shared mental model situations.

Findings

The algorithm effectively identifies belief discrepancies.

Agents can communicate to resolve plan feasibility issues.

The approach scales well with team size and complexity.

Abstract

When agents collaborate on a task, it is important that they have some shared mental model of the task routines -- the set of feasible plans towards achieving the goals. However, in reality, situations often arise that such a shared mental model cannot be guaranteed, such as in ad-hoc teams where agents may follow different conventions or when contingent constraints arise that only some agents are aware of. Previous work on human-robot teaming has assumed that the team has a set of shared routines, which breaks down in these situations. In this work, we leverage epistemic logic to enable agents to understand the discrepancy in each other's beliefs about feasible plans and dynamically plan their actions to adapt or communicate to resolve the discrepancy. We propose a formalism that extends conditional doxastic logic to describe knowledge bases in order to explicitly represent agents' nested beliefs on the feasible plans and state of execution. We provide an online execution algorithm based on Monte Carlo Tree Search for the agent to plan its action, including communication actions to explain the feasibility of plans, announce intent, and ask questions. Finally, we evaluate the success rate and scalability of the algorithm and show that our agent is better equipped to work in teams without the guarantee of a shared mental model.