Sarath Sreedharan, Tathagata Chakraborti, et al.
AAAI 2020
Dead-end detection is a key challenge in automated planning, and it is rapidly growing in popularity. Effective dead-end detection techniques can have a large impact on the strength of a planner, and so the effective computation of dead-ends is central to many planning approaches. One of the better understood techniques for detecting dead-ends is to focus on the delete relaxation of a planning problem, where dead-end detection is a polynomial-time operation. In this work, we provide a logical characterization for not just a single dead-end, but for every delete-relaxed dead-end in a planning problem. With a logical representation in hand, one could compile the representation into a form amenable to effective reasoning. We lay the ground-work for this larger vision and provide a preliminary evaluation to this end.
Sarath Sreedharan, Tathagata Chakraborti, et al.
AAAI 2020
Alberto Camacho, Jorge A. Baier, et al.
ICAPS 2018
Joseph Kim, Christian Muise, et al.
IJCAI 2019
Christian Muise
COPLAS 2018