Kstar Planner is an Automated PDDL based planner that includes planners for top-k and top-quality planning computational tasks.

The codebase consists of multiple planners, for multiple computational problems, roughly divided into two categories:

Top-k planning
Top-quality planning
2.1. Top-quality planning
2.2. Unordered top-quality planning

The planner implements

Pure K* search based top-k and top-quality planning
Symmetry based pruning: OK* search
Partial order reduction: RK* search Both symmetry based pruning and partial order reduction can be used together: ORK* search

Installation

pip install kstar-planner

Running (examples)

Top-k

OK* with lmcut heuristic

from kstar_planner import planners
from pathlib import Path

domain_file = Path("your/path/domain.pddl")
problem_file = Path("your/path/problem.pddl")

plans = planners.plan_topk(domain_file=domain_file, problem_file=problem_file, number_of_plans_bound=100, timeout=30)
print(plans)

Top-quality

OK* with iPDB heuristic (recommended)

from kstar_planner import planners
from pathlib import Path

domain_file = Path("your/path/domain.pddl")
problem_file = Path("your/path/problem.pddl")

heuristic = "ipdb(transform=undo_to_origin())"

plans = planners.plan_topq(domain_file=domain_file, problem_file=problem_file, quality_bound=1.0,number_of_plans_bound=100, timeout=30, search_heuristic=heuristic)
print(plans)

Unordered Top-quality

ORK* with iPDB heuristic (recommended)

from kstar_planner import planners
from pathlib import Path

domain_file = Path("your/path/domain.pddl")
problem_file = Path("your/path/problem.pddl")

heuristic = "ipdb(transform=undo_to_origin())"

plans = planners.plan_unordered_topq(domain_file=domain_file, problem_file=problem_file, quality_bound=1.0,number_of_plans_bound=100, timeout=30, search_heuristic=heuristic)
print(plans)

Citing

Top-k planning

@InProceedings{lee-et-al-socs2023,
  title =        "On K* Search for Top-k Planning",
  author =       "Junkyu Lee and Michael Katz and Shirin Sohrabi",
  booktitle =    "Proceedings of the 16th Annual Symposium on
                  Combinatorial Search (SoCS 2023)",
  publisher =    "{AAAI} Press",
  year =         "2023"
}

@InProceedings{katz-lee-ijcai2023,
  author =       "Michael Katz and Junkyu Lee",
  title =        "K* Search Over Orbit Space for Top-k Planning",
  booktitle =    "Proceedings of the 32nd International Joint
                  Conference on Artificial Intelligence (IJCAI 2023)",
  publisher =    "{IJCAI}",
  year =         "2023"
}

Top-quality planning

@InProceedings{katz-lee-socs2023,
  title =        "K* and Partial Order Reduction for Top-quality Planning",
  author =       "Michael Katz and Junkyu Lee",
  booktitle =    "Proceedings of the 16th Annual Symposium on
                  Combinatorial Search (SoCS 2023)",
  publisher =    "{AAAI} Press",
  year =         "2023"
}

Licensing

Kstar Planner is an Automated PDDL based planner that includes planners for top-k and top-quality planning computational tasks. Copyright (C) 2023 Junkyu Lee, Michael Katz, IBM Research, USA. The code extends the Fast Downward planning system. The license for the extension is specified in the LICENSE file.

Fast Downward

Fast Downward is a domain-independent classical planning system.

For further information:

Fast Downward website: https://www.fast-downward.org
Report a bug or file an issue: https://issues.fast-downward.org
Fast Downward mailing list: https://groups.google.com/forum/#!forum/fast-downward
Fast Downward main repository: https://github.com/aibasel/downward

Tested software versions

This version of Fast Downward has been tested with the following software versions:

OS	Python	C++ compiler	CMake
Ubuntu 20.04	3.8	GCC 9, GCC 10, Clang 10, Clang 11	3.16
Ubuntu 18.04	3.6	GCC 7, Clang 6	3.10
macOS 10.15	3.6	AppleClang 12	3.19
Windows 10	3.6	Visual Studio Enterprise 2017 (MSVC 19.16) and 2019 (MSVC 19.28)	3.19

We test LP support with CPLEX 12.9, SoPlex 3.1.1 and Osi 0.107.9. On Ubuntu, we test both CPLEX and SoPlex. On Windows, we currently only test CPLEX, and on macOS, we do not test LP solvers (yet).

Contributors

The following list includes all people that actively contributed to Fast Downward, i.e. all people that appear in some commits in Fast Downward's history (see below for a history on how Fast Downward emerged) or people that influenced the development of such commits. Currently, this list is sorted by the last year the person has been active, and in case of ties, by the earliest year the person started contributing, and finally by last name.

2003-2022 Malte Helmert
2008-2016, 2018-2022 Gabriele Roeger
2010-2022 Jendrik Seipp
2010-2011, 2013-2022 Silvan Sievers
2012-2022 Florian Pommerening
2013, 2015-2022 Salomé Eriksson
2018-2022 Patrick Ferber
2021-2022 Clemens Büchner
2021-2022 Dominik Drexler
2022 Remo Christen
2015, 2021 Thomas Keller
2016-2020 Cedric Geissmann
2017-2020 Guillem Francès
2018-2020 Augusto B. Corrêa
2020 Rik de Graaff
2015-2019 Manuel Heusner
2017 Daniel Killenberger
2016 Yusra Alkhazraji
2016 Martin Wehrle
2014-2015 Patrick von Reth
2009-2014 Erez Karpas
2014 Robert P. Goldman
2010-2012 Andrew Coles
2010, 2012 Patrik Haslum
2003-2011 Silvia Richter
2009-2011 Emil Keyder
2010-2011 Moritz Gronbach
2010-2011 Manuela Ortlieb
2011 Vidal Alcázar Saiz
2011 Michael Katz
2011 Raz Nissim
2010 Moritz Goebelbecker
2007-2009 Matthias Westphal
2009 Christian Muise

History

The current version of Fast Downward is the merger of three different projects:

the original version of Fast Downward developed by Malte Helmert and Silvia Richter
LAMA, developed by Silvia Richter and Matthias Westphal based on the original Fast Downward
FD-Tech, a modified version of Fast Downward developed by Erez Karpas and Michael Katz based on the original code

In addition to these three main sources, the codebase incorporates code and features from numerous branches of the Fast Downward codebase developed for various research papers. The main contributors to these branches are Malte Helmert, Gabi Röger and Silvia Richter.

License

The following directory is not part of Fast Downward as covered by this license:

./src/search/ext

For the rest, the following license applies:

Fast Downward is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or (at
your option) any later version.

Fast Downward is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.

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kstar-planner 1.6.0

Kstar Planner is an Automated PDDL based planner that includes planners for top-k and top-quality planning computational tasks.

The codebase consists of multiple planners, for multiple computational problems, roughly divided into two categories:

Installation

Running (examples)

Top-k

Top-quality

Unordered Top-quality

Citing

Top-k planning

Top-quality planning

Licensing

Fast Downward

Tested software versions

Contributors

History

License

Project links

Key dates

2 maintainers

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Algorithm	Hash digest
SHA256	`5edb4f31d39c02c778f938f3e9fa8779433104e8abd677d22d5ed783b9b6c81a`
MD5	`bb000cefabfc0891194313d3e481f276`
BLAKE2b-256	`ef3ce7791ef06a2c65d9f5ce7de36e40f7838004a9ea7f63a14753394bfbe56b`

Algorithm	Hash digest
SHA256	`0ce8feb431e2cbcb645409606d81e8787696662c359fb57ce863802134cebcae`
MD5	`9354454d03f8490580ce959aa352d014`
BLAKE2b-256	`5ad367e3800166dbbb2547811c77f705515ccb7dc49e44ea9424d13eea7df94a`

Algorithm	Hash digest
SHA256	`3a05798ed58176b01c3303af8651027fe92c796c5cb65e3a79c29968292028f2`
MD5	`3fa38f1d4ddc5fee22c67a5f602cafb4`
BLAKE2b-256	`6c5bd0bf0e5a90ce0a25a7e23da3dec2eaced9e11e7964175fd8651ce25fb955`

Algorithm	Hash digest
SHA256	`0c8493fee7f99e869718b46d9c5b72f55debf03d443586be3a18e2124b519215`
MD5	`dd2c607593aa9c8f991c7002d2ff8b07`
BLAKE2b-256	`95fe16f79d151847302b506b21c5195bcf528aa837465e52843accddf721883c`

Algorithm	Hash digest
SHA256	`e72a0cb538d51194b7a7c301ad01e25a3fee6a90dbadb51ff16ef41dc47ccbc9`
MD5	`ea4fd85ec234e2bc465b689876e4ebcf`
BLAKE2b-256	`108dfa7d19577f5843d1a26a6b4b4adac8b516dc689b15170eeb7c772f35e40a`

Algorithm	Hash digest
SHA256	`4f7f33d243a71ff2fc5cbe1156ac75afc3c025c1e61051a9d5a9d21a8b877360`
MD5	`d32db6d986e6be98069c8c3201272a73`
BLAKE2b-256	`a71412504beac7863adcf70b02afa77fca4b0fb49667de80b6e5991c4debe38f`

Algorithm	Hash digest
SHA256	`acee69633a593acc24c5241bc3019da71eabee1b52e9ee99f65006fbca5f408f`
MD5	`8857a8613485c3a7aac87167f2df66e0`
BLAKE2b-256	`643ee2e7e62a4950fa1105e1b2365f89f1b367d8f845f45f3a51bf2b401899df`

Algorithm	Hash digest
SHA256	`c9cca7662aa123add545154aa8cf8de00ff88c3e788548679e728bc62df999e6`
MD5	`44fb48a2749f9c82bab128fe6c643bef`
BLAKE2b-256	`5b51d7ec501a5af6df80b534aa81893a7bf3134657357f07c9ab01fcfbd04246`

Algorithm	Hash digest
SHA256	`fc1121ca7a4647ad7f3715b272d210d0e99da530b445ade1a10293a0e71a5e4b`
MD5	`4d471a93e144db303fb9e562b3e2fe67`
BLAKE2b-256	`116d46dfe19b1ccba0de17143bdb4ac6b7c68e817a6d4829455359a4b9213853`