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

arXiv: https://arxiv.org/abs/2307.03362 · HTML: https://mlsanity.com/paper/2307.03362/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 7 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Plan Explicability and Predictability for Robot Task Planning](https://mlsanity.com/paper/1511.08158/) (2015)
- [NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants](https://mlsanity.com/paper/2301.05223/) (2023)
- [Cooperative Epistemic Multi-Agent Planning for Implicit Coordination](https://mlsanity.com/paper/1703.02196/) (2017)
- [Expectation-Aware Planning: A Unifying Framework for Synthesizing and Executing Self-Explaining Plans for Human-Aware Planning](https://mlsanity.com/paper/1903.07269/) (2019)
- [Towards the Role of Theory of Mind in Explanation](https://mlsanity.com/paper/2005.02963/) (2020)
- [A Mental-Model Centric Landscape of Human-AI Symbiosis](https://mlsanity.com/paper/2202.09447/) (2022)

## Built on by (most cited first, 1 of 1)

- [Human-Modeling in Sequential Decision-Making: An Analysis through the Lens of Human-Aware AI](https://mlsanity.com/paper/2405.07773/) (2024)

## Abstract (arXiv)

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.