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Creators/Authors contains: "Vasileiou, Stylianos L"

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  1. ; ; ( , XAIP@ICAPS 2020)
    null (Ed.)
    In this paper, we build upon notions from knowledge representation and reasoning (KR) to expand a preliminary logic-based framework that characterizes the model reconciliation problem for explainable planning. We also provide a detailed exposition on the relationship between similar KR techniques, such as abductive explanations and belief change, and their applicability to explainable planning. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ( , International Conference on Principles of Knowledge Representation and Reasoning ()
    null (Ed.)
    In human-aware planning problems, the planning agent may need to explain its plan to a human user, especially when the plan appears infeasible or suboptimal for the user. A popular approach to do so is called model reconciliation, where the planning agent tries to reconcile the differences between its model and the model of the user such that its plan is also feasible and optimal to the user. This problem can be viewed as an optimization problem, where the goal is to find a subset-minimal explanation that one can use to modify the model of the user such that the plan of the agent is also feasible and optimal to the user. This paper presents an algorithm for solving such problems using answer set programming. 
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    Accepted Manuscript Full Text Available