Abstract
AbstractIn the context of planning and reasoning about actions and change, we call an action reversible when its effects can be reverted by applying other actions, returning to the original state. Renewed interest in this area has led to several results in the context of the PDDL language, widely used for describing planning tasks. In this paper, we propose several solutions to the computational problem of deciding the reversibility of an action. In particular, we leverage an existing translation from PDDL to Answer Set Programming (ASP), and then use several different encodings to tackle the problem of action reversibility for the STRIPS fragment of PDDL. For these, we use ASP, as well as Epistemic Logic Programming (ELP), an extension of ASP with epistemic operators, and compare and contrast their strengths and weaknesses.
Highlights
The field of Automated Planning deals with the problem of generating a sequence of actions – a plan – that transforms an initial state of the environment to some goal state, see for instance the papers by Ghallab et al . (2004; 2016)
We have given a review of several notions of action reversibility in STRIPS planning, as originally presented by Morak et al . (2020)
On the basis of the PDDL-to-Answer Set Programming (ASP) translation tool plasp, described by Dimopoulos et al . (2019), to present two Epistemic Logic Programming (ELP) encodings and two ASP encodings to solve the task of universal uniform reversibility of STRIPS actions, given a corresponding planning domain
Summary
The field of Automated Planning deals with the problem of generating a sequence of actions – a plan – that transforms an initial state of the environment to some goal state, see for instance the papers by Ghallab et al . (2004; 2016). An action a is applicable in a state s iff pre(a) ⊆ s. The result of applying an action a in a state s, given that a is applicable in s, is the state a[s] = (s \ del (a)) ∪ add (a). An is applicable in a state s0 iff there is a sequence of states s1, .
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