RT Conference Proceedings
T1 Efficiently reasoning with interval constraints in forward search planning
A1 Coles, Amanda
A1 Coles, Andrew
A1 Martínez Muñoz, Moises
A1 Delfa, Juan Manuel
A1 Rosa Turbides, Tomás Eduardo de la
A1 Escudero Martín, Yolanda
A1 García Olaya, Ángel
AB In this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting.
PB Aaai Press. Association For The Advancement Of Artificial Intelligence
SN 9781577358091
SN 2159-5399
YR 2019
FD 2019-01-27
LK https://hdl.handle.net/10016/30448
UL https://hdl.handle.net/10016/30448
LA eng
NO 27 de enero - 1 de febrero 2019, Hilton Hawaiian Village, Honolulu,Hawaii, USA
NO This work has received funding from the European Union’s Horizon 2020 Research and Innovation programme (Grant Agreement 730086, ERGO); EPSRC grant EP/P008410/1(AI Planning with Continuous Non-Linear Change); the European Space Agency (ESA/ESTEC) GOTCHA project, Contract No. 4000117648/16/NL/GLC/fk; and Ministeriode Economía, Industria y Competitividad TIN2017-88476-C2-2-R and TIN2015-65686-C5.
DS e-Archivo
RD 17 jul. 2024