Probabilistic Plan Legibility with Off-the-shelf Planners

Abstract:Legible planning is the creation of plans that best disambiguate their goals from a set of other candidates from an observer's perspective. In this paper we propose a method for legible planning for arbitrary PDDL domains, by extending previous research on legibility to classical planning without requiring to construct ad-hoc planners. We also discuss how the observer perspective may be estimated through a second order theory of mind that connects the planner's and the observer's task spaces. Our solution can for example be deployed in human-robot teaming scenarios, where an autonomous robot in a team can implicitly communicate its goal by producing legible plans. We present benchmark results on several PDDL planning domains. Our results generally show that plan legibility is a trade-off with plan efficiency, however, not all planning domains allows to increase legibility in the same way and a regularizing factor to balance legibility and efficiency was proved necessary.

Accepted at the 9th ICAPS Workshop on Planning and Robotics. ICAPS 2021

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2610.00065 [cs.RO]

(or arXiv:2610.00065v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.00065 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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