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Sep 29, 2017 · In this paper we consider the opportunities that arise in AI planning, exploiting the model-based representations that form a familiar and ...
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This paper presents Explainable Planning (XAIP), describ- ing some initial results, and proposing a roadmap for making. XAIP more effective and efficient. Of ...
Specifically, the framework addresses two types of queries: reason-seeking queries, which explain the reasoning behind scheduling decisions, and modification- ...
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In this demonstration, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human-in-the-loop ...
Explainable Planning: Explainable planning as dis- cussed by Chakraborti et al. (2017) assumes that a plan- ning problem P = (I,G,D) is given, and it is ...
AI Planning in particular is relevant in this context as a generic approach to action-decision problems. Indeed, explainable AI Planning (XAIP) has received ...
Feb 26, 2020 · Abstract:In this paper, we provide a comprehensive outline of the different threads of work in Explainable AI Planning (XAIP) that has ...
In this paper, we argue that Explainable Planning can be designed as a service -- that is, as a wrapper around an existing planning system that utilises the ...
In this paper, we propose that Explainable Planning can be designed and constructed as a service – i.e., as a wrap- per around an existing planning system that ...