The Model Reconciliation Problem (MRP) was introduced to address issues in explainable AI planning. A solution to a MRP is an explanation for the differences between the models of the human and the planning agent (robot). Most approaches to solving MRPs assume that the robot, who needs to provide explanations, knows the human model. This assumption is not always realistic in several situations (e.g., the human might decide to update her model and the robot is unaware of the updates). In this paper, we propose a dialog-based approach for computing explanations of MRPs under the assumptions that (i) the robot does not know the human model; (ii) the human and the robot share the set of predicates of the planning domain and their exchanges are about action descriptions and fluents' values; (iii) communication between the parties is perfect; and (iv) the parties are truthful. A solution of a MRP is computed through a dialog, defined as a sequence of rounds of exchanges, between the robot and the human. In each round, the robot sends a potential explanation, called proposal, to the human who replies with her evaluation of the proposal, called response. We develop algorithms for computing proposals by the robot and responses by the human and implement these algorithms in a system that combines imperative means with answer set programming using the multi-shot feature of clingo.

本文提出了一种基于对话的方法来计算模型协调问题的解决方案，该方法假定机器人不知道人类模型，机器人和人类共享计划域的谓词集，并且交流的内容是关于动作描述和流形值的交换。通过对话，机器人向人类发送潜在的解释，被称为提案，人类回复其提案的评估，被称为响应。

关于模型调节：当机器人不知道人类的模型时如何调节？