论文标题
关于模型和解：当机器人不知道人的模型时，如何和解？
On Model Reconciliation: How to Reconcile When Robot Does not Know Human's Model?
论文作者
论文摘要
引入了模型对帐问题（MRP），以解决可解释的AI计划中的问题。 MRP的解决方案是对人与计划代理（机器人）模型之间差异的解释。解决MRP的大多数方法都认为，需要提供解释的机器人知道人类模型。在几种情况下，这个假设并不总是现实的（例如，人可能会决定更新她的模型，并且机器人不知道更新）。 在本文中，我们提出了一种基于对话的方法，用于计算MRP的解释，即（i）机器人不知道人类模型； （ii）人类和机器人共享计划领域的谓词集，其交流是关于行动描述和流利的价值； （iii）双方之间的沟通是完美的； （iv）当事方是真实的。 MRP解决方案是通过对话框计算的，该对话框定义为机器人和人之间的一系列交换序列。在每回合中，机器人都向人类发送了一个潜在的解释，称为提案，她对提案的评估回答称为回应。我们开发了用于计算机器人和人类响应的算法，并在将命令式手段与使用Clingo的多拍功能的答案集编程相结合的系统中实现了这些算法。
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.
