Model-Free Model Reconciliation
Model-Free Model Reconciliation
复制标题
无模型模型协调
DOI：
10.24963/ijcai.2019/83
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发表时间：
2019
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;Alberto Olmo Hernandez;A. Mishra;S. Kambhampati
设计能够解释复杂顺序决策的代理仍然是人类与AI交互中的一个重要开放问题。最近，有很多兴趣在发展的方法产生这样的解释，为各种决策模式。其中一种方法是将解释作为模型调和的想法。该框架假设，一个用户的困惑的常见原因之一可能是代理的任务模型的用户模型和代理所使用的模型生成的决定之间的不匹配。虽然这是一个一般的框架，大多数作品已经明确建立在这种解释性的哲学集中在经典的规划设置，用户的知识模型是在一个声明的形式。我们在本文中的目标是适应模型协调的方法，更一般的规划范例，并讨论如何使用这样的方法时，用户模型不再明确可用。具体来说，我们提出了一个简单易学的标签模型，可以帮助解释者决定什么信息可以帮助实现用户和代理之间的模型和解的背景下，规划与MDP。
Designing agents capable of explaining complex sequential decisions remains a significant open problem in human-AI interaction. Recently, there has been a lot of interest in developing approaches for generating such explanations for various decision-making paradigms. One such approach has been the idea of explanation as model-reconciliation. The framework hypothesizes that one of the common reasons for a user's confusion could be the mismatch between the user's model of the agent's task model and the model used by the agent to generate the decisions. While this is a general framework, most works that have been explicitly built on this explanatory philosophy have focused on classical planning settings where the model of user's knowledge is available in a declarative form. Our goal in this paper is to adapt the model reconciliation approach to a more general planning paradigm and discuss how such methods could be used when user models are no longer explicitly available. Specifically, we present a simple and easy to learn labeling model that can help an explainer decide what information could help achieve model reconciliation between the user and the agent with in the context of planning with MDPs.