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تسجيل مستخدم جديدProgressive Explanation Generation for Human-robot Teaming
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Generating explanation to explain its behavior is an essential capability for a robotic teammate. Explanations help human partners better understand the situation and maintain trust of their teammates. Prior work on robot generating explanations focuses on providing the reasoning behind its decision making. These approaches, however, fail to heed the cognitive requirement of understanding an explanation. In other words, while they provide the right explanations from the explainers perspective, the explainee part of the equation is ignored. In this work, we address an important aspect along this direction that contributes to a better understanding of a given explanation, which we refer to as the progressiveness of explanations. A progressive explanation improves understanding by limiting the cognitive effort required at each step of making the explanation. As a result, such explanations are expected to be smoother and hence easier to understand. A general formulation of progressive explanation is presented. Algorithms are provided based on several alternative quantifications of cognitive effort as an explanation is being made, which are evaluated in a standard planning competition domain.
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As AI becomes an integral part of our lives, the development of explainable AI, embodied in the decision-making process of an AI or robotic agent, becomes imperative. For a robotic teammate, the ability to generate explanations to justify its behavio
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Prior work on generating explanations in a planning and decision-making context has focused on providing the rationale behind an AI agents decision making. While these methods provide the right explanations from the explainers perspective, they fail
to heed the cognitive requirement of understanding an explanation from the explainees (the humans) perspective. In this work, we set out to address this issue by first considering the influence of information order in an explanation, or the progressiveness of explanations. Intuitively, progression builds later concepts on previous ones and is known to contribute to better learning. In this work, we aim to investigate similar effects during explanation generation when an explanation is broken into multiple parts that are communicated sequentially. The challenge here lies in modeling the humans preferences for information order in receiving such explanations to assist understanding. Given this sequential process, a formulation based on goal-based MDP for generating progressive explanations is presented. The reward function of this MDP is learned via inverse reinforcement learning based on explanations that are retrieved via human subject studies. We first evaluated our approach on a scavenger-hunt domain to demonstrate its effectively in capturing the humans preferences. Upon analyzing the results, it revealed something more fundamental: the preferences arise strongly from both domain dependent and independence features. The correlation with domain independent features pushed us to verify this result further in an escape room domain. Results confirmed our hypothesis that the process of understanding an explanation was a dynamic process. The human preference that reflected this aspect corresponded exactly to the progression for knowledge assimilation hidden deeper in our cognitive process.
Providing explanations is considered an imperative ability for an AI agent in a human-robot teaming framework. The right explanation provides the rationale behind an AI agents decision-making. However, to maintain the human teammates cognitive demand
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