Scorpion is an optimal classical planner that uses saturated cost partitioning to combine multiple abstraction heuristics. It also contains implementations of many other cost partitioning algorithms over abstraction and landmark heuristics. Scorpion is based on the Fast Downward planning system (version 21.12), which is described below. We regularly port the latest changes from Fast Downward to Scorpion and also try to port Scorpion features back to Fast Downward.

Please use the following reference when citing Scorpion: Jendrik Seipp, Thomas Keller and Malte Helmert. Saturated Cost Partitioning for Optimal Classical Planning. Journal of Artificial Intelligence Research 67, pp. 129-167. 2020.

After installing the requirements (see below), compile the planner with

./build.py

and see the available options with

./fast-downward.py --help  # driver
./fast-downward.py --search -- --help  # search component

For more details (including build instructions for Windows), see the documentation about compiling and running the planner. The plugin documentation shows which plugins are available (heuristics, search algorithms, etc.) and how to use them.

We recommend using the following configuration:

./fast-downward.py \
  --transform-task preprocess-h2 \
  ../benchmarks/gripper/prob01.pddl \
  --search "astar(scp_online([
        projections(sys_scp(max_time=100, max_time_per_restart=10)),
        cartesian()],
        saturator=perimstar, max_time=1000, interval=10K, orders=greedy_orders()),
        pruning=limited_pruning(pruning=atom_centric_stubborn_sets(), min_required_pruning_ratio=0.2))"

The preprocess-h2 call prunes irrelevant operators in a preprocessing step. The search configuration uses partial order reduction and maximizes over diverse, subset-saturated cost partitioning heuristics computed online during the search. The underlying abstractions are Sys-SCP pattern databases and Cartesian abstractions.

(In Downward Lab you can use add_algorithm(name="scorpion", repo="path/to/repo", rev="scorpion", component_options=[], driver_options=["--transform-task", "preprocess-h2", "--alias", "scorpion"] to run the recommended Scorpion configuration.)

To simplify the installation process, we provide an executable Singularity container for Scorpion. It accepts the same arguments as the fast-downward.py script (see above).

# Download the container (tested with Singularity 3.5),
singularity pull scorpion.sif library://jendrikseipp/default/scorpion:latest

# or build the container yourself.
sudo singularity build scorpion.sif Singularity

# Then run recommended configuration (available via "scorpion" alias).
./scorpion.sif --transform-task preprocess-h2 --alias scorpion PROBLEM_FILE

If you prefer to run the Scorpion version from IPC 2018 (which uses an older Fast Downward version and different abstractions), we recommend using the Scorpion IPC repo.

• Scorpion comes with the h²-preprocessor by Vidal Alcázar and Álvaro Torralba that prunes irrelevant operators. Pass --transform-task preprocess-h2 to use it.
• The --transform-task command allows you to run arbitrary preprocessing commands that transform the SAS+ output from the translator before passing it to the search.
• Scorpion uses a phmap::flat_hash_set to check for duplicate states, which often drastically reduces the peak memory usage, compared to Fast Downward's IntHashSet.
• If ccache is installed (recommended), Scorpion uses it to cache compilation files.

• cegar(..., search_strategy=incremental): use incremental search for Cartesian abstraction refinement (default).

• hillclimbing(..., max_generated_patterns=200): limit the number of patterns generated by hill climbing.

• systematic(..., pattern_type=interesting_general): compute interesting patterns for general cost partitioning.

We use Cartesian abstractions in the example configurations below ([cartesian()]). You can also use pattern database heuristics, e.g., [projections(systematic(2))], or mix abstractions, e.g., [projections(systematic(3)), cartesian()]. Some of the algorithms below are also part of vanilla Fast Downward, but are only implemented for PDB heuristics.

• Optimal cost partitioning: ocp([cartesian()])
• Canonical heuristic: canonical_heuristic([cartesian()])
• Uniform cost partitioning: ucp([cartesian()], opportunistic=false)
• Opportunistic uniform cost partitioning: ucp([cartesian()], ..., opportunistic=true)
• Greedy zero-one cost partitioning: gzocp([cartesian()], ...)
• Saturated cost partitioning: scp([cartesian()], ...) (offline), scp_online([cartesian()], ...) (online)
• (Saturated) post-hoc optimization: pho([cartesian()], ..., saturated={false,true}) (offline), operatorcounting([pho_abstraction_constraints([cartesian()], saturated={false,true})]) (online)

You can also compute the maximum over abstraction heuristics:

• maximize([cartesian()])

The plugin documentation shows all options for cost partitioning heuristics.

• Systematic patterns with size limits: sys_scp(max_pattern_size=X, max_pdb_size=Y, max_collection_size=Z, ..., saturate=false)
• Sys-SCP patterns: sys_scp(...)

Example using A* search and saturated cost partitioning over BJOLP landmarks:

--evaluator
  "lmc=lmcount(lm_merged([lm_rhw(), lm_hm(m=1)]),
  admissible=true, cost_partitioning=suboptimal, greedy=true,
  reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)"
--search
  "astar(lmc, lazy_evaluator=lmc)"

Different cost partitioning algorithms (all need admissible=true):

• Optimal cost partitioning (part of vanilla Fast Downward): lmcount(..., cost_partitioning=optimal)
• Canonical heuristic: lmcount(..., cost_partitioning=canonical)
• Post-hoc optimization: lmcount(..., cost_partitioning=pho)
• Uniform cost partitioning: lmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=false)
• Opportunistic uniform cost partitioning (part of vanilla Fast Downward): lmcount(..., cost_partitioning=suboptimal, greedy=false, reuse_costs=true, scoring_function=min_stolen_costs)
• Greedy zero-one cost partitioning: lmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=false, scoring_function=max_heuristic)
• Saturated cost partitioning: lmcount(..., cost_partitioning=suboptimal, greedy=true, reuse_costs=true, scoring_function=max_heuristic_per_stolen_costs)

• Breadth-first search (without overhead of the more general eager() search): brfs()
• Depth-first search: dfs()
• IDA* search: idastar(cegar(cache_estimates=false))
• Iterative width search: iw(width=2)

Fast Downward is a domain-independent classical planning system.

Copyright 2003-2022 Fast Downward contributors (see below).

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

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

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).

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

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.

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/>.