Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation
Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation
复制标题
通过模型协调处理解释中的模型不确定性和多重性
DOI：
10.1609/icaps.v28i1.13930
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
S. Kambhampati
中科院分区：
文献类型：
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作者：
S. Sreedharan;Tathagata Chakraborti;S. Kambhampati
模型协调被认为是一种代理向对同一计划问题有不同理解的人解释其决策的一种方式，通过用这些模型差异来解释其决策。然而，通常人类的心理模型(以及由此产生的差异)是不准确的，并且这种解释不容易计算。在本文中，我们展示了解释生成过程是如何在存在这种模型不确定性或不完备性的情况下进化的，通过生成适用于一组可能的模型的符合解释}，我们还展示了这些解释如何包含多余的信息，以及如何使用条件解释与人类迭代以获得共同点来减少这些冗余。最后，我们将介绍这种方法的随时版本，并经验地演示了不同形式的解释在代理的计算开销和人类的通信开销方面所涉及的权衡。我们在三个著名的规划领域以及在一个机器人上的演示中展示了这些概念，该机器人参与了一个典型的具有外部人类主管的搜索和侦察场景。
Model reconciliation has been proposed as a way for an agent to explain its decisions to a human who may have a different understanding of the same planning problem by explaining its decisions in terms of these model differences.However, often the human's mental model (and hence the difference) is not known precisely and such explanations cannot be readily computed.In this paper, we show how the explanation generation process evolves in the presence of such model uncertainty or incompleteness by generating {\em conformant explanations} that are applicable to a set of possible models.We also show how such explanations can contain superfluous informationand how such redundancies can be reduced using conditional explanations to iterate with the human to attain common ground. Finally, we will introduce an anytime version of this approach and empirically demonstrate the trade-offs involved in the different forms of explanations in terms of the computational overhead for the agent and the communication overhead for the human.We illustrate these concepts in three well-known planning domains as well as in a demonstration on a robot involved in a typical search and reconnaissance scenario with an external human supervisor.