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Back to Releases.

Fast Downward 21.12

Fast Downward 21.12 was released on February 16, 2022.

Highlights:

  • Fast Downward now has a logo!
  • We added new methods for generating patterns and pattern collections based on counterexample-guided abstraction refinement and a new highly random method for generating individual patterns based on the causal graph. These methods are due to Rovner et al. (ICAPS 2019).

  • The operator-counting heuristic now has an option to use integer operator counts rather than real-valued operator counts. This makes the heuristic more accurate at a vastly increased computational cost (not generally recommended, but very useful for targeted experiments). We added a new constraint generator for Imai and Fukunaga's delete relaxation constraints (JAIR 2015). With the right option settings, the operator-counting heuristic with this new constraint generator results in the optimal delete relaxation heuristic h+.

  • Pruning methods now have a different interface. The mechanism to disable pruning automatically after a number of expansions that resulted in little pruning is now implemented as its own pruning method that wraps another pruning method. Be careful that the old syntax is still accepted by the planner, but the options that limit pruning are ignored. (This is due to an option parser bug; a fix is in the works.)
  • In our ongoing efforts to improve the landmark code, the landmark factories and landmark-count heuristic received a major overhaul. We removed irrelevant options for landmark factories, decoupled the computation of reasonable orders from landmark generation, made many internal code and data structure changes to make the code nicer to work with and fixed several long-standing bugs.
  • All pattern generators and pattern collection generators now have controllable verbosity. Similar changes to other components of the planner are planned. This is part of a general effort to make logging more configurable.

  • For developers: the internal representation of states has been overhauled, resolving the confusion between the previous classes GlobalState and State.

The Singularity and Docker distributions of the planner include LP support using the SoPlex solver. Thank you to ZIB for their solver and for giving permission to include it in the release. SoPlex is licensed under the ZIB Academic License.

Downloads

For Singularity, Docker and Vagrant, see QuickStart for instructions on how to run the planner. That page explains how to run the latest release of Fast Downward, so you have to amend the instructions if you want to run an older release.

For the tarball, if you have satisfied all dependencies, the following should build and run the planner with configuration alias lama-first on PDDL files /path/to/domain.pddl and /path/to/problem.pddl on a Linux or macOS system: 

tar -xvzf fast-downward-21.12.tar.gz
cd fast-downward-21.12
./build.py
./fast-downward.py --alias lama-first /path/to/domain.pddl /path/to/problem.pddl

For dependencies and more advanced information (including Windows builds), see ObtainingAndRunningFastDownward.

Limitations

For licensing reasons, the binary distributions of Fast Downward have limited support for commercial LP solvers (CPLEX and Gurobi). The Docker and Singularity distributions only support SoPlex. The Vagrant version supports SoPlex and/or CPLEX if they are available at provisioning time. See QuickStart for more information.

Referencing Fast Downward 21.12

If you use this version of Fast Downward in a scientific publication, we encourage you to mention the version number, as in "We ran experiments with Fast Downward 21.12." The canonical reference for citing Fast Downward is the following journal paper: 

@Article{helmert-jair2006,
  author =  "Malte Helmert",
  title =   "The {Fast} {Downward} Planning System",
  journal = "Journal of Artificial Intelligence Research",
  year =    "2006",
  volume =  "26",
  pages =   "191--246"
}

Changes in Fast Downward 21.12

FastDownward: Releases/21.12 (last edited 2023-10-12 12:04:36 by FlorianPommerening)