We propose a method that allows to develop shared understanding between two agents for the purpose of performing a task that requires cooperation. Our method focuses on efficiently establishing successful task-oriented communication in an open multi-agent system, where the agents do not know anything about each other and can only communicate via grounded interaction. The method aims to assist researchers that work on human-machine interaction or scenarios that require a human-in-the-loop, by defining interaction restrictions and efficiency metrics. To that end, we point out the challenges and limitations of such a (diverse) setup, while also restrictions and requirements which aim to ensure that high task performance truthfully reflects the extent to which the agents correctly understand each other. Furthermore, we demonstrate a use-case where our method can be applied for the task of cooperative query answering. We design the experiments by modifying an established ontology alignment benchmark. In this example, the agents want to query each other, while representing different databases, defined in their own ontologies that contain different and incomplete knowledge. Grounded interaction here has the form of examples that consists of common instances, for which the agents are expected to have similar knowledge. Our experiments demonstrate successful communication establishment under the required restrictions, and compare different agent policies that aim to solve the task in an efficient manner.

翻译：我们提出了一种方法，旨在使两个智能体在需要协作的任务中发展共享理解。该方法聚焦于在开放多智能体系统中高效建立面向任务的成功通信，其中智能体彼此一无所知，且仅能通过接地交互进行通信。该方法通过定义交互限制与效率指标，旨在为从事人机交互或需要人类参与场景的研究人员提供支持。为此，我们指出了这种（多样化）设置的挑战与局限，同时提出了确保高任务性能真实反映智能体相互理解程度的限制条件与要求。此外，我们展示了一个可应用该方法进行协作查询回答任务的使用案例。通过修改已有的本体对齐基准测试，我们设计了实验。在此示例中，智能体代表不同数据库（各自使用包含不同且不完整知识的自有本体进行定义）相互查询。接地交互形式表现为包含共同实例的示例，期望智能体对这些实例具有相似知识。实验证明，在所需限制条件下成功建立了通信，并比较了旨在高效解决任务的不同智能体策略。