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We propose a new benchmark for planning tasks based on the Minecraft game.
PDDL – The Planning Domain Definition Language
McDermott, D.; Ghallab, M.; Howe, A.; Knoblock, C.; Ram, A.; Veloso, M.; Weld, D.; and Wilkins, D. 1998 · 1998
Earlier work this paper cites.
PDDL2.1: An extension to PDDL for expressing temporal planning domains
Fox, M.; and Long, D. 2003 · 2003
Earlier work this paper cites.
Learning Probabilistic Relational Planning Rules
Pasula, H.; Zettlemoyer, L. S.; and Kaelbling, L. P. 2004 · 2004
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The fast downward planning system
Helmert, M. 2006 · 2006
Earlier work this paper cites.
Task Scoping: Generating Task-Specific Abstractions for Planning in Open-Scope Models
Fishman, M.; Kumar, N.; Allen, C.; Danas, N.; Littman, M.; Tellex, S.; and Konidaris, G. 2020 · 2010
Earlier work this paper cites.
Minecraft as an experimental world for AI in robotics
Aluru, K. C.; Tellex, S.; Oberlin, J.; and MacGlashan, J. 2015 · 2015
Earlier work this paper cites.
The Malmo Platform for Artificial Intelligence Experimentation
Johnson, M.; Hofmann, K.; Hutton, T.; and Bignell, D. 2016 · 2016
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Interval-based relaxation for general numeric planning
Scala, E.; Haslum, P.; Thiebaux, S.; and Ramirez, M. 2016 · 2016
Earlier work this paper cites.
Mastering the game of Go with deep neural networks and tree search
Silver, D.; Huang, A.; Maddison, C. J.; Guez, A.; Sifre, L.; Van Den Driessche, G.; Schrittwieser, J.; Antonoglou, I.; Panneershelvam, V.; Lanctot, M.; et al. 2016 · 2016
Earlier work this paper cites.
Singularity: Scientific containers for mobility of compute
Kurtzer, G. M.; Sochat, V.; and Bauer, M. W. 2017 · 2017
Earlier work this paper cites.
Automated planning with goal reasoning in Minecraft
Roberts, M.; Piotrowski, W.; Bevan, P.; Aha, D.; Fox, M.; Long, D.; and Magazzeni, D. 2017 · 2017
Cited alongside, same era.
Classical planning in deep latent space: Bridging the subsymbolic-symbolic boundary
Asai, M.; and Fukunaga, A. 2018 · 2018
Cited alongside, same era.
From skills to symbols: Learning symbolic representations for abstract high-level planning
Konidaris, G.; Kaelbling, L. P.; and Lozano-Pérez, T. 2018 · 2018
Cited alongside, same era.
MineRL: a large-scale dataset of Minecraft demonstrations
Guss, W. H.; Houghton, B.; Topin, N.; Wang, P.; Codel, C.; Veloso, M.; and Salakhutdinov, R. 2019 · 2019
Cited alongside, same era.
Generalized planning via abstraction: arbitrary numbers of objects
Illanes, L.; and McIlraith, S. 2019 · 2019
Cited alongside, same era.
Construction-planning models in Minecraft
Wichlacz, J.; Torralba, A.; and Hoffmann, J. 2019 · 2019
Subgoaling techniques for satisficing and optimal numeric planning
Scala, E.; Haslum, P.; Thiébaux, S.; and Ramirez, M. 2020 · 2020
Later among the works it cites.
PDDLGym: Gym Environments from PDDL Problems
Silver, T.; and Chitnis, R. 2020 · 2020
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Polynomial-Time in PDDL input size: Making the delete relaxation feasible for lifted planning
Lauer, P.; Torralba, A.; Fišer, D.; Höller, D.; Wichlacz, J.; and Hoffmann, J. 2021 · 2021
Later among the works it cites.
Classical Planning as QBF without Grounding
Shaik, I.; and van de Pol, J. 2021 · 2021
Later among the works it cites.
Planning with learned object importance in large problem instances using graph neural networks
Silver, T.; Chitnis, R.; Curtis, A.; Tenenbaum, J. B.; Lozano-Pérez, T.; and Kaelbling, L. P. 2021 · 2021
Later among the works it cites.
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Cited alongside, same era.
Lifted successor generation using query optimization techniques
Corrêa, A. B.; Pommerening, F.; Helmert, M.; and Frances, G. 2020 · 2020
Cited alongside, same era.
HDDL: An extension to PDDL for expressing hierarchical planning problems
Höller, D.; Behnke, G.; Bercher, P.; Biundo, S.; Fiorino, H.; Pellier, D.; and Alford, R. 2020 · 2020
Cited alongside, same era.
Learning portable representations for high-level planning
James, S.; Rosman, B.; and Konidaris, G. 2020 · 2020
Cited alongside, same era.
The AI settlement generation challenge in Minecraft: First year report
Salge, C.; Green, M. C.; Canaan, R.; Skwarski, F.; Fritsch, R.; Brightmoore, A.; Ye, S.; Cao, C.; and Togelius, J. 2020 · 2020
Cited alongside, same era.
Deepsym: Deep symbol generation and rule learning for planning from unsupervised robot interaction
Ahmetoglu, A.; Seker, M. Y.; Piater, J.; Oztop, E.; and Ugur, E. 2022 · 2022
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Minedojo: Building open-ended embodied agents with internet-scale knowledge
Fan, L.; Wang, G.; Jiang, Y.; Mandlekar, A.; Yang, Y.; Zhu, H.; Tang, A.; Huang, D.-A.; Zhu, Y.; and Anandkumar, A. 2022 · 2022
Later among the works it cites.
Autonomous learning of object-centric abstractions for high-level planning
James, S.; Rosman, B.; and Konidaris, G. 2022 · 2022
Later among the works it cites.
Hierarchically Composing Level Generators for the Creation of Complex Structures
Beukman, M.; Fokam, M.; Kruger, M.; Axelrod, G.; Nasir, M.; Ingram, B.; Rosman, B.; and James, S. 2023 · 2023
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Predicate Invention for Bilevel Planning
Silver, T.; Chitnis, R.; Kumar, N.; McClinton, W.; Lozano-Pérez, T.; Kaelbling, L. P.; and Tenenbaum, J. 2023 · 2023
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