Polynomial-Time in PDDL Input Size: Making the Delete Relaxation Feasible for Lifted Planning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00350205" target="_blank" >RIV/68407700:21230/21:00350205 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.24963/ijcai.2021/567" target="_blank" >https://doi.org/10.24963/ijcai.2021/567</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24963/ijcai.2021/567" target="_blank" >10.24963/ijcai.2021/567</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Polynomial-Time in PDDL Input Size: Making the Delete Relaxation Feasible for Lifted Planning

Popis výsledku v původním jazyce

Polynomial-time heuristic functions for planning are commonplace since 20 years. But polynomial-time in which input? Almost all existing approaches are based on a grounded task representation, not on the actual PDDL input which is exponentially smaller. This limits practical applicability to cases where the grounded representation is "small enough". Previous attempts to tackle this problem for the delete relaxation leveraged symmetries to reduce the blow-up. Here we take a more radical approach, applying an additional relaxation to obtain a heuristic function that runs in time polynomial in the size of the PDDL input. Our relaxation splits the predicates into smaller predicates of fixed arity K. We show that computing a relaxed plan is still NP-hard (in PDDL input size) for K>=2, but is polynomial-time for K=1. We implement a heuristic function for K=1 and show that it can improve the state of the art on benchmarks whose grounded representation is large.

Název v anglickém jazyce

Polynomial-Time in PDDL Input Size: Making the Delete Relaxation Feasible for Lifted Planning

Popis výsledku anglicky

Polynomial-time heuristic functions for planning are commonplace since 20 years. But polynomial-time in which input? Almost all existing approaches are based on a grounded task representation, not on the actual PDDL input which is exponentially smaller. This limits practical applicability to cases where the grounded representation is "small enough". Previous attempts to tackle this problem for the delete relaxation leveraged symmetries to reduce the blow-up. Here we take a more radical approach, applying an additional relaxation to obtain a heuristic function that runs in time polynomial in the size of the PDDL input. Our relaxation splits the predicates into smaller predicates of fixed arity K. We show that computing a relaxed plan is still NP-hard (in PDDL input size) for K>=2, but is polynomial-time for K=1. We implement a heuristic function for K=1 and show that it can improve the state of the art on benchmarks whose grounded representation is large.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

ISBN

978-0-9992411-9-6

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

4119-4126

Název nakladatele

International Joint Conferences on Artificial Intelligence Organization

Místo vydání

—

Místo konání akce

Montreal

Datum konání akce

19. 8. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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