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Obtaining and running Fast Downward

If you want to:

then this page is for you.

Since we don't currently consider the code to be in releasable shape, there is no "official" code distribution of the planner available at the moment. You can still get access to the planner, but it's important that you are aware of some issues with the current codebase.

/!\ Important note: Make sure to completely read the #Caveats section below.

/!\ Note on running LAMA: As of 2011, LAMA has been merged back into the Fast Downward codebase. "LAMA 2011", the version of LAMA that participated in IPC 2011, is Fast Downward with a particular set of command-line arguments. Since LAMA 2011 greatly outperformed LAMA 2008 in the competition, we strongly encourage you to use the current Fast Downward code when evaluating your planner against LAMA. See the following note.

/!\ Note on running IPC 2011 planners based on Fast Downward: After familiarizing yourself with the basics on this page, please check out IpcPlanners.

/!\ License: While our repository does not currently contain licensing information, the planner is made available under the GNU Public License (GPL). If you want to use the planner in any way that is not compatible with the GPL, you will have to get permission from us.

Supported platforms

Linux

Linux is the main platform for which the planner is developed. All features should work without restrictions under Linux.

Mac OS X

The planner contains the necessary code to compile and run on Mac OS X, but since we do not use this platform, we cannot promise that it works as well as Linux. When running serious experiments with Mac OS X, we strongly suggest that you do at least some basic comparison to Linux to make sure that all results are in order and planner performance doesn't degrade. We appreciate bug reports and patches for this platform, including contributions to the documentation (e.g. installation instructions).

Windows

The same comments as for Mac OS X apply. Moreover, some diagnostic features, such as reporting memory used when the planner is terminated by a signal, are not supported under Windows. Due to library limitations setting time and memory limits and running portfolios is not supported under Windows. We appreciate bug reports and patches for this platform, including contributions to the documentation (e.g. installation instructions).

Getting access to the planner

In the following we will assume Ubuntu Linux or a similar environment. If you use other kinds of Linux or other Unix-ish systems, you should be able to adapt the following steps to your setting. Some basic instructions for Windows are also available. If you use a different system, you will probably not be able to compile the planner.

Since October 2010, the main development of the code takes place in a Mercurial repository. Before then, the code was hosted in a Subversion repository, which is still available. However, the trunk of the Subversion repository has been frozen (set to read-only) and will no longer receive updates or bug fixes. See LegacySubversionRepository if you need to access the old repository for some reason.

If you haven't used Mercurial before, you may want to have a look at the excellent documentation available online (also available as a printed book published by O'Reilly Media). However, you don't really have to understand Mercurial to use the planner. For those experienced with Mercurial or similar distributed version control systems, we currently use a "single-pusher" task-based workflow with a single pusher for the Fast Downward repository. If you want to contribute to Fast Downward, the recommended way is to set up a clone of the master repository (e.g. on Bitbucket) and provide a link to it in our issue tracker so that we can pull from it.

Dependencies

To obtain and build the planner, you need any version of Mercurial, a C++11 compiler, CMake and GNU make. We have successfully compiled the planner with GCC 4.8 and Clang 3.3/3.4. For planner comparisons we recommend using GCC 4.8 since the produced code runs faster than Clang 3.3's code on the IPC benchmarks we tested (we haven't yet tested the performance of Clang 3.4 and later). To run the planner, you also need Python 2.7 or Python >= 3.2. These dependencies are often satisfied by default on development machines. If not,

sudo apt-get install cmake g++ g++-multilib mercurial make python

should be sufficient.

LP Solver

Some configurations require an LP solver to work. The planner will compile fine if there is no LP installed on the system, but trying to use the features that require an LP solver will generate an error message explaining what is missing. See LPBuildInstructions for instructions on how to set up an LP solver and tell Fast Downward about it.

Validator VAL

You can validate the found plans by passing --validate to the planner. This invokes VAL (https://github.com/KCL-Planning/VAL). Here are the steps for setting up VAL for the planner:

   1 sudo apt-get install g++ make flex bison
   2 git clone https://github.com/KCL-Planning/VAL.git
   3 cd VAL
   4 make clean  # Remove old build artifacts and binaries.
   5 make
   6 # Add "validate" binary to a directory on your PATH.
   7 

Building VAL on Windows

You will need to download Flex and Bison.

Building VAL on Mac OS X

If your compiler doesn't find flex or bison, your include directories might be in a non-standard location. In this case you probably have to specify where to look for includes and libraries in VAL's Makefile (probably /Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr).

Additional dependencies on Mac OS X

As of this writing, the planner appears to run without problems on Mac OS X. However, we do not promise full support.

Dependencies on Windows

On Windows, you should install Visual Studio (we tested with the free VS 2013 Express Community Edition), Python, Mercurial, and CMake. If you use Visual Studio 2015, make sure to install the C++ compiler. The compiler is not installed by default, but the IDE will prompt you to install it when you create a new C++ project.

Obtaining the code

The command

hg clone http://hg.fast-downward.org DIRNAME

will create a clone of the Fast Downward master repository in directory DIRNAME. The directory is created if it does not yet exist. In the following, we assume that you used downward as the DIRNAME.

Compiling the planner

To build the planner for the first time, run:

   1 cd downward
   2 ./build.py

This will create our default build release32 in the directory downward/builds. Other predefined build types are debug32, release64, and debug64. (Please see PlannerUsage#64bit for caveats for 64-bit planners.) Calling ./build.py --debug will create a default debug build (equivalent to debug32). You can pass make parameters to ./build.py, e.g., ./build.py --debug -j4 will create a debug build using 4 threads for compilation (-j4), and ./build.py translate will only build the translator component. (Because the translator is implemented in Python, "building" it just entails copying its source code into the build directory.)

See #Manual_Builds for more complex builds.

Compiling on Mac OS X

Fast Downward should compile with the GNU C++ compiler and clang with the same instructions described above. In case your system compiler does not work for some reason, you will need to install the GNU compiler (if not already present) and tell CMake which compiler to use (see #ManualBuilds).

Compiling on Windows

Windows does not interpret the shebang in python files, so you have to call build.py as python build.py (make sure python is on your PATH). The steps above should work if you execute them in the developer console of Visual Studio which will use nmake and cl for the build. For Visual Studio 2013 there should be a shortcut for x86 native in C:\Program Files (x86)\Microsoft Visual Studio 12.0\Common7\Tools\Shortcuts. For Visual Studio 2015 you have to open a regular shell (cmd) and call the script C:\Program Files (x86)\Microsoft Visual Studio 14.0\Common7\Tools\VsDevCmd.bat to set up the build environment.

Alternatively, you can create a Visual Studio Project (see #ManualBuilds), open it in Visual Studio and build from there. Visual Studio will create its binary files in subdirectories of the project that our driver script currently does not recognize. If you build with Visual Studio, you will have to run the individual components of the planner yourself.

Manual Builds

The build.py script only creates a directory and calls cmake and make. To do these steps manually, run:

   1 mkdir -p builds/mycustombuild
   2 cd builds/mycustombuild
   3 cmake ../../src CMAKE_OPTIONS
   4 make

where CMAKE_OPTIONS are the options used to configure the build (see below). Without options, this results in the release32 build. (Use --build mycustombuild in the fast-downward.py script to select this build when running the planner.)

You can use a CMake GUI to set up all available options. To do so, on Unix-like systems replace the call to cmake by ccmake (sudo apt-get install cmake-curses-gui). On Windows, open the CMake GUI and enter the paths there.

Possible options to configure the build include:

You can also generate makefiles for other build systems (such as ninja) or generate project files for most IDEs:

You can also change the compiler/linker by setting the environment variables CC, CXX and LINKER. For example, to compile with clang-3.5 use:

   1 CC=clang-3.5 CXX=clang-3.5 cmake ../../src

Use full paths if the compiler is not found on the PATH, e.g., to force using the GNU compiler on Mac OS X using HomeBrew:

   1 CXX=/usr/local/Cellar/gcc48/4.8.3/bin/g++-4.8 CC=/usr/local/Cellar/gcc48/4.8.3/bin/g++-4.8 LINKER=/opt/local/bin/g++-mp-4.8 cmake ../../src

The final example creates a 64-bit build with the GNU compiler using MacPorts:

   1 CXX=/opt/local/bin/g++-mp-4.8 CC=/opt/local/bin/gcc-mp-4.8 LINKER=/opt/local/bin/g++-mp-4.8 cmake ../../src -DALLOW_64_BIT=YES

If you use a configuration often, it might make sense to add an alias for it in build.py.

Running the planner

For basic instructions on how to run the planner including examples, see PlannerUsage. The search component of the planner accepts a host of different options with widely differing behaviour. At the very least, you will want to choose a search engine with one or more heuristic specifications.

Caveats

Please be aware of the following issues when working with the planner, especially if you want to use it for conducting scientific experiments:

  1. You are working with the most recent version of the development branch of the code, which is usually not thoroughly tested. Things can break or degrade with every commit. Typically they don't, but if they do, don't be surprised. We are working on a proper release, but we are not there yet. For more information, see the list of outstanding issues for release 1.0 of the planner.

  2. There are known bugs, especially with the translator component. To find out more, check out our issue tracker. The planner has only really been tested with IPC domains, and even for those it does not work properly with all formulations of all domains. For more information, see the section on #Known_good_domains below.

  3. The search options are built with flexibility in mind, not ease of use. It is very easy to use option settings that look plausible, yet introduce significant inefficiencies. For example, an invocation like

    ./fast-downward.py domain.pddl problem.pddl --search "lazy_greedy(ff(), preferred=ff())"

    looks plausible, yet is hugely inefficient since it will compute the FF heuristic twice per state. See the examples on the PlannerUsage page to see how to call the planner properly. If in doubt, ask.

Known good domains

There is a large collection of planning competition benchmarks at https://bitbucket.org/aibasel/downward-benchmarks, which includes all IPC domains (but not all formulations of all domains). The planner is somewhat sensitive to non-STRIPS language constructs and will choke on some valid PDDL inputs. Moreover, many of the heuristics do not support axioms or conditional effects. Even worse, sometimes the translator will introduce conditional effects even though the original PDDL input did not contain them. (We are working on this.)

We recommend that you use the sets of domains that are predefined in the suites.py script for your experiments. Here are the benchmark suites that we usually use in the two most common settings:

After cloning the repo, you can list the domains in the respective suites by doing:

   1 ./suites.py optimal_strips

You can use the resulting list of domains in your experiment scripts (see below).

Experiments with Fast Downward

The lab python library has a module downward that helps running Fast Downward experiments on large benchmark sets. → More information

FastDownward: ObtainingAndRunningFastDownward (last edited 2016-12-21 15:55:21 by MalteHelmert)