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

Pascal Lauer, Alvaro Torralba, Daniel Fišer, Daniel Höller, Julia Wichlacz, Jörg Hoffmann
2021 Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence   unpublished
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.  ...  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.  ...  Acknowledgments This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -Project-ID 232722074 -SFB 1102.  ... 
doi:10.24963/ijcai.2021/567 fatcat:rawsjdhq6ff5pdygdhshswj5oe

Learning How to Ground a Plan – Partial Grounding in Classical Planning

Daniel Gnad, Álvaro Torralba, Martín Domínguez, Carlos Areces, Facundo Bustos
Current classical planners are very successful in finding (nonoptimal) plans, even for large planning instances.  ...  We propose a guiding mechanism that, for a given domain, identifies the parts of a task that are relevant to find a plan by using off-the-shelf machine learning methods.  ...  DA/16/01 "Optimizing Planning Domains".  ... 
doi:10.1609/aaai.v33i01.33017602 fatcat:quqmajjg5bclnp3dc6nqvy7geq

On Succinct Groundings of HTN Planning Problems

Gregor Behnke, Daniel Höller, Alexander Schmid, Pascal Bercher, Susanne Biundo
In this paper we present a new approach for grounding HTN planning problems that produces smaller groundings in a shorter timespan than the previously published method.  ...  For HTN planning models, only one method to ground lifted models has been published so far.  ...  Acknowledgements This work was partially funded by the technology transfer project "Do it yourself, but not alone: Companion-Technology for DIY support" of the SFB/TRR 62 funded by the German Research  ... 
doi:10.1609/aaai.v34i06.6529 fatcat:mjr6gt52sfehdo2xoaccfkboxe

Learning action models from plan examples using weighted MAX-SAT

Qiang Yang, Kangheng Wu, Yunfei Jiang
2007 Artificial Intelligence  
AI planning requires the definition of action models using a formal action and plan description language, such as the standard Planning Domain Definition Language (PDDL), as input.  ...  However, building action models from scratch is a difficult and time-consuming task, even for experts.  ...  Acknowledgement We wish to thank the Hong Kong RGC grant 621606 for supporting this research.  ... 
doi:10.1016/j.artint.2006.11.005 fatcat:nitcegmzw5d6lffvjhr2r76lqe

Engineering Benchmarks for Planning: the Domains Used in the Deterministic Part of IPC-4

J. Hoffmann, S. Edelkamp, S. Thiebaux, R. Englert, F. Liporace, S. Trueg
2006 The Journal of Artificial Intelligence Research  
of the relaxed plan heuristic).  ...  For the first time in the IPC, we used compilations to formulate complex domain features in simple languages such as STRIPS, rather than just dropping the more interesting problem constraints in the simpler  ...  ) due to the increase in plan size.  ... 
doi:10.1613/jair.1982 fatcat:hmne5ycxj5gjtn5d2u5hz73dqu

Efficient approaches for multi-agent planning

Daniel Borrajo, Susana Fernández
2018 Knowledge and Information Systems  
In the first distributed approach, agents iteratively solve problems by receiving plans, goals and states from previous agents.  ...  In the second centralized approach, agents generate an obfuscated version of their problems to protect privacy and then submit it to an agent that performs centralized planning.  ...  We would like to thank the help and making the code available to Alejandro Torreño, Raz Nissim, Mathew Crosby and Antonín Komenda.  ... 
doi:10.1007/s10115-018-1202-1 fatcat:gmjehdfddrhp7ppsj4eu3mzd4a

From OpenCCG to AI Planning: Detecting Infeasible Edges in Sentence Generation

Maximilian Schwenger, Álvaro Torralba, Jörg Hoffmann, David M. Howcroft, Vera Demberg
2016 International Conference on Computational Linguistics  
We design a compilation from OpenCCG into AI Planning allowing the detection of infeasible edges via AI Planning dead-end detection methods (proving the absence of a solution to the compilation).  ...  Formulating the completion of an edge into a sentence as finding a solution path in a large state-transition system, we demonstrate a connection to AI Planning which is concerned with this kind of problem  ...  Acknowledgements This work was partially supported by the DFG excellence cluster EXC 284 "Multimodal Computing and Interaction" and the DFG collaborative research center SFB 1102 "Information Density and  ... 
dblp:conf/coling/SchwengerTHHD16 fatcat:qf4y7l5wjzf3lbvnkix45nunmm

The FF Heuristic for Lifted Classical Planning

Augusto B. Corrêa, Florian Pommerening, Malte Helmert, Guillem Francès
Heuristics for lifted planning are not yet as informed as the best heuristics for ground planning.  ...  In our experiments, we show that a planner using the lifted FF implementation produces state-of-the-art results for lifted planners.  ...  Acknowledgments This work was funded by the Swiss National Science Foundation (SNSF) as part of the project "Certified Correctness and Guaranteed Performance for Domain-Independent Planning" (CCGP-Plan  ... 
doi:10.1609/aaai.v36i9.21206 fatcat:aa2bwsehwzfqfnyrqktd2nnxy4

Safe Learning of Lifted Action Models [article]

Brendan Juba, Hai S. Le, Roni Stern
2021 arXiv   pre-print
We generalize this prior work and propose the first safe model-free planning algorithm for lifted domains.  ...  Prior work proposed an algorithm for solving the model-free planning problem in classical planning. However, they were limited to learning grounded domains, and thus they could not scale.  ...  that is linear in the possible size of the lifted model.  ... 
arXiv:2107.04169v1 fatcat:2jyqe4bvunczlgiy5uvpl2lpk4

Modelling Mixed Discrete-Continuous Domains for Planning

M. Fox, D. Long
2006 The Journal of Artificial Intelligence Research  
In this paper we present pddl+, a planning domain description language for modelling mixed discrete-continuous planning domains.  ...  We describe the syntax and modelling style of pddl+, showing that the language makes convenient the modelling of complex time-dependent effects.  ...  We would also like to thank Subbarao Kambhampati and the anonymous referees for helping us to organise and clarify the presentation of this work, and Jeremy Frank, Stefan Edelkamp, Nicola Muscettola,  ... 
doi:10.1613/jair.2044 fatcat:dohrf3ualrhbpedhpi6a7k4kwi

Deterministic planning in the fifth international planning competition: PDDL3 and experimental evaluation of the planners

Alfonso E. Gerevini, Patrik Haslum, Derek Long, Alessandro Saetti, Yannis Dimopoulos
2009 Artificial Intelligence  
high quality plans in metric-time domains and domains involving soft goals or constraints.  ...  The international planning competition (IPC) is an important driver for planning research.  ...  Acknowledgements We would like to thank the anonymous reviewers for many useful comments. The organisers of IPC5, Yannis Dimopoulos, Alfonso E. Gerevini  ... 
doi:10.1016/j.artint.2008.10.012 fatcat:nnm56wydxvemljdamo7anawx3e

Taming Numbers and Durations in the Model Checking Integrated Planning System

S. Edelkamp
2003 The Journal of Artificial Intelligence Research  
The linear time algorithm to compute the parallel plan bypasses known NP hardness results for partial ordering by scheduling plans with respect to the set of actions and the imposed precedence relations  ...  Plans were no longer sequences of actions but time-stamped schedules.  ...  Acknowledgments The author would like to thank Derek Long and Maria Fox for helpful discussions concerning this paper and Malte Helmert for his cooperation in the second planning competition.  ... 
doi:10.1613/jair.1302 fatcat:h3uxy2ithfb4fhqlcyeup4p2vu

Fact-Alternating Mutex Groups for Classical Planning

Daniel Fišer, Antonín Komenda
2018 The Journal of Artificial Intelligence Research  
can be in the translation of planning tasks into finite domain representation.  ...  Mutex groups are defined in the context of STRIPS planning as sets of facts out of which, maximally, one can be true in any state reachable from the initial state.  ...  Computational resources were provided by the CESNET LM2015042 and the CERIT Scientific Cloud LM2015085, provided under the programme "Projects of Large Research, Development, and Innovations Infrastructures  ... 
doi:10.1613/jair.5321 fatcat:kn3u64mr6rahncxnszrqxcangq

ITSAT: An Efficient SAT-Based Temporal Planner

Masood Feyzbakhsh Rankooh, Gholamreza Ghassem-Sani
2015 The Journal of Artificial Intelligence Research  
However, this approach has not been competitive with the state-space based methods in temporal planning.  ...  We also show how, as in SAT-based classical planning, carefully devised preprocessing and encoding schemata can considerably improve the efficiency of SAT-based temporal planning.  ...  Acknowledgments The authors would like to thank the handling editor, Jörg Hoffmann, and the anonymous reviewers for their invaluable contributions to the quality of this paper.  ... 
doi:10.1613/jair.4697 fatcat:ojs6ptmmz5ecdodue3gsuuxwx4

Abstraction for non-ground answer set programs

Zeynep G. Saribatur, Thomas Eiter, Peter Schüller
2021 Artificial Intelligence  
This approach was used in planning for speeding up the solving [64] and especially for computing heuristic functions to guide the plan search in the state space.  ...  In this work, we introduce a notion for abstracting from the domain of an ASP program such that the domain size shrinks while the set of answer sets (i.e., models) of the program is over-approximated.  ...  Acknowledgements We are grateful to the reviewers for their helpful and constructive comments to improve this work and its presentation.  ... 
doi:10.1016/j.artint.2021.103563 fatcat:nyhzwgry3vc4tbwaci6g2aoqay
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