An Efficient Incremental Simple Temporal Network Data Structure for Temporal Planning

• URL: http://arxiv.org/abs/2212.07226v2
• Date: Fri, 11 Aug 2023 13:59:47 GMT
• Title: An Efficient Incremental Simple Temporal Network Data Structure for Temporal Planning
• Authors: Andrea Micheli
• Abstract summary: One popular technique to solve temporal planning problems consists in decoupling the causal decisions, demanding them to search, from temporal decisions, demanding them to a simple temporal network (STN) solver. In this paper, we describe in detail how STNs are used in temporal planning, we identify a clear interface to support this use-case and we present an efficient data-structure implementing this interface that is both time- and memory-efficient. We show that our data structure, called deltastn, is superior to other state-of-the-art approaches on temporal planning sequences of problems.
• Score: 7.835452825434851
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: One popular technique to solve temporal planning problems consists in decoupling the causal decisions, demanding them to heuristic search, from temporal decisions, demanding them to a simple temporal network (STN) solver. In this architecture, one needs to check the consistency of a series of STNs that are related one another, therefore having methods to incrementally re-use previous computations and that avoid expensive memory duplication is of paramount importance. In this paper, we describe in detail how STNs are used in temporal planning, we identify a clear interface to support this use-case and we present an efficient data-structure implementing this interface that is both time- and memory-efficient. We show that our data structure, called \deltastn, is superior to other state-of-the-art approaches on temporal planning sequences of problems.

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