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.

翻译：当智能体协作执行任务时，建立关于任务规程（即实现目标的可执行计划集）的共享心理模型至关重要。然而实际情境中，此类共享心理模型常无法保证——例如在临时组建的团队中，成员可能遵循不同协议，或因突发约束条件仅有部分智能体知晓。先前的人机团队研究假设团队具备共享规程库，这在此类情境下会失效。本研究利用认知逻辑使智能体能够理解彼此对可行计划信念的差异，并动态规划行动以通过适应或沟通消解差异。我们提出一种扩展条件性信念逻辑的形式化方法，通过描述知识库显式表征智能体对可行计划及执行状态的嵌套信念。基于蒙特卡洛树搜索的在线执行算法被用于智能体决策规划，包括阐释计划可行性的沟通行动、意图宣告及疑问提出。最终，我们评估了算法的成功率和可扩展性，证明该智能体在无共享心理模型保障的团队中具备更优协作能力。