International Planning Competition 2018
Temporal Tracks

This is the website for the temporal tracks of the IPC 2018. This is the 7th IPC containing classical tracks, since the specification of PDDL 2.1 in 2002. In addition to the temporal tracks, the IPC also has classical and probabilistic tracks. You can find information about them on

Mailing List:

Preliminary Schedule

Event Date
Call for domains / expression of interest June 22, 2017
Domain submission deadline November, 2017
Demo problems provided January, 2017
Initial planner submission February 18, 2018
Feature stop (final planner submission) March 18, 2018
Planner Abstract submission deadline April, 2018
Contest run April - May, 2018
Results announced June, 2018
Result analysis deadline July, 2018


There are two temporal tracks: 'satisficing' and 'on board'. The tracks differ in the amount of resources available. The evaluation criterion is plan quality within the time available. We encourage participants to enter their planner in both tracks.

Satisficing Planning Track

  • single CPU core
  • 8Gb memory limit
  • 30min time limit
  • Multiple plans can be returned, the one with the lowest cost is counted.
  • The score of a planner on a solved task is the ratio C*/C where C is the cost of the cheapest discovered plan and C* is the cost of a reference plan. The score on an unsolved task is 0. The score of a planner is the sum of its scores for all tasks.
  • If an invalid plan is returned, all tasks in the domain are counted as unsolved.
  • If that happens in more than one domain, the entry is disqualified.

On-Board Planning Track

  • designed as an analogue for on-board planning, for use where the resources available are much less than on a desktop computer; the time limit is chosen to mock up 30 minutes on low-power hardware
  • single CPU core
  • 1Gb memory limit
  • 2min time limit
  • Multiple plans can be returned, the one with the lowest cost is counted.
  • Scoring as in the satisficing track.
  • If an invalid plan is returned, all tasks in the domain are counted as unsolved.
  • If that happens in more than one domain, the entry is disqualified.

PDDL Fragment

IPC 2018 will use a subset of PDDL 2.2, as in previous years. Planners must support the subset of the language involving STRIPS, durative actions and action costs. Some domains will additionally require Timed Initial Literals, and numeric state variables. We will consider providing alternative domain definitions where practical.


As in previous editions, the competitors must submit the source code of their planners that will be run by the organizers on the actual competition domains/problems, unknown to the competitors until this time. This way no fine-tuning of the planners will be possible.

All competitors must submit an abstract (max. 300 words) and a 4-page paper describing their planners. After the competition we encourage the participants to analyze the results of their planner and submit an extended version of their abstract. An important requirement for IPC 2018 competitors is to give the organizers the right to post their paper and the source code of their planners on the official IPC 2018 web site.

Registration Process

We will use the container technology "Singularity" this year to promote reproducibility and help with compilation issues that have caused problems in the past. More details on Singularity can be found below.

To register your planner create a repository (mercurial and git repositories are accepted) on bitbucket and give read access to ipc2018-temporal-bot. Then create one branch per track you want to participate in and name it according to the following list.

  • ipc-2018-temp-sat (satisficing track)
  • ipc-2018-temp-on-board (on board track)
Up to two versions of the same planner are allowed to participate. To submit two different versions of the same planner, simply create two different repositories. In each branch, add a file called Singularity to the root directory of your repository. This file is used to bootstrap a singularity container and to run the planner (an example can be found in our demo submission. For more details on Singularity see below).

We will build all planners once a day and run them on a number of test cases. You can see the results for your planner on the build status page. Test your Singularity file locally (see below) and make sure it passes our automated tests.

A planner is officially registered in a track if it has a green box for that track on the build status page on March 18. You can still make any code changes you want until April 10. The build status on the website will update (once a day) when you push new changes the registered branches.

Bug fixing policy

We will fork your repository on February 20. If you find any bugs in your code afterwards (or if we detect any while running your code), you can create a pull request to our fork with a patch fixing the bug. Only bug fixes will be accepted after the deadline (in particular, we will not accept patches modifying behavior or tuning parameters).

Details on Singularity

In an effort to increase reproducibility and reduce the effort of running future IPCs, we are using software containers that contain the submitted planner and everything required to run it. We are using Singularity which is an alternative to the well-known Docker. Singularity (in contrast to Docker) is specifically designed for scientific experiments on HPC clusters and has low overhead.

Singularity containers can be viewed as light-weight alternatives to virtual machines that carry a program and all parts of the OS that are necessary to run it. They can be based on any docker image. We created an example submission ( Singularity file) that uses the latest Ubuntu as a basis and uses apt-get to install required packages for Fast Downward. It then builds the planner from the files that are next to the Singularity file in the repository.

In the following, we collect and answer frequently asked questions about Singularity. We'll update this section as we get more questions. If you run into problems using Singularity and your problem is not answered here, let us know.

We will use version 2.4. This version has some new features that are useful for our setup. For example, the size of the container does not have to be specified when building it.

You can install Singularity locally with the following commands (See the Singularity quick start guide for more details):

  sudo apt install automake
  git clone
  cd singularity
  git checkout 2.4
  ./configure --prefix=/usr/local
  sudo make install                         

To test your Singularity script, please install Singularity (see above) and run the following commands (replacing our demo submission with your repository):

   hg clone -r ipc-2018-temp-sat
   sudo singularity build planner.img demo-submission/Singularity
   mkdir rundir
   cp path/to/domain.pddl rundir
   cp path/to/problem.pddl rundir
   singularity run -C -H $RUNDIR planner.img $DOMAIN $PROBLEM $PLANFILE

The last command also shows how we will call the container during the competition: the parameter "-H" mounts the directory containing the PDDL files into the file system of the container and uses it as the user's home directory. The parameter "-C" then isolates the container from the rest of the system. Only files written to the mounted directory will be stored permanently. Other created files (for example in /tmp) only persist for the current session and are cleaned up afterwards. When running the container on two instances at the same time, their run directories and sessions will be different, so the two runs cannot interact. The container itself is read-only after its creation.

We will also build your code about once per day and show the results for all planners on the build status page

Yes but only to certain directories. The runscript of your container is started from the home directory of the container which is also the directory that contains the input files. You have write access to this directory and files written here will be persistent. However, the home directory will be different in each run, so each run will start from the same clean container. You also have write access to the directory
but files written there will be deleted after the run. See the question above for how to set up Singularity in this way for testing.
Yes only the Bitbucket user ipc2018-temporal-bot needs access. However, the build status page will show logs for all registered planners. We consider this information public.

Please contact us if your license does not permit you to package the library into the container.

If we can acquire a license, we will mount the installation files for the library while building the container. You can then copy the installation file into the container in the %setup step and install it in the %post step.

We currently have a license for CPLEX and make the installer for version 12.7.1 (64 bit) available during setup. You can see an example of the installation in the multi-core track of our demo submission. For all other libraries, please get in touch with us as soon as possible.

This is technically possible but please don't do this. Your submission has to include your source code and should be built from that code. For increased reproducibility please make your repository as self-contained as possible.
Our example submission ( Singularity file) shows how to copy your source code into the container. Using the same %setup section will copy the files from the correct branch into the container. In the %post section you can then build your planner in the directory /planner.
No, we will get an automated notification about this and will add you to the list of teams. Your planner should show up on the build status page after one or two days. If your planner doesn't show up on the build status page, or if you have any other questions or problems, you are of course welcome to contact us. Please add the list of authors, name and description of the planner and other meta data to your Singularity file according to our example Singularity file.
Singularity images can be based on any docker image. We used a basic Ubuntu image as the basis of our demo but you are welcome to use other images. The Singularity image must run on CentOS but most unix-based images will work. Windows and OS X are not supported. If you are trying to generate a small image, a lightweight OS such as Alpine Linux might be an option. However, be aware that Alpine Linux uses musl instead of glibc.
After you compile your planner, you may remove the planner source code and packages that are only required for building your planner. Our demo submission shows this in the multi-core track.
It is not necessary to reduce the image size as much as possible but we appreciate any effort to keep the images small.
This can be caused by Windows line endings in the Singularity file. The line Bootstrap: docker is then parsed as Bootstrap: docker\r and not recognized. Using Linux-style line endings should fix the issue.

Input/Output Rules

Input: The runscript specified in the Singularity file should accept 3 parameters (4 in the cost bounded track):

./runscript <domain> <problem> <plan-output-file> [<cost-bound>]

Output: The planner must write the plan file(s) in the location specified in <plan-output-file>, except in the satisficing track where plans can also be written in files <plan-output-file>.1, <plan-output-file>.2, <plan-output-file>.3, etc.

Random Seed: We won't provide any random seed as part of the input to the planners. However, for the sake of replicability of the experiments, we ask all participants to fix a random seed and make their planner behaviour as deterministic as possible.

Optional Log Output: As part as the competition results we would like to publish detailed data about the participant planners that helps to understand the competition results in more detail. For that, we would like your collaboration in printing some stats about your planner that can be parsed afterwards. This is completely optional and we will not rely on this data to determine the winner. We will parse the following information:

  • Time format: all times in the following output are optional and can be given in different formats.
    • Absolute (time passed since the planner started): "[732.736s]"
    • Relative (relative to last printed time): "[+32.736s]"
    • Absolute reset (all absolute times afterwards are relative to this time): "[=+732.736s]"
  • Plan cost bounds: print a line as soon as new lower/upper bound is found
    • "proven lower bound: 124 TIME"
    • "proven upper bound: 250 TIME"
  • Portfolio planner techniques: print when a technique is started/ended (can also be used for preprocess/translate) and which technique solved the problem
    • "starting technique: NAME TIME"
    • "technique finished: NAME TIME"
    • "most useful technique: NAME"
  • Task size: print statistics on the problem (potentially after preprocessing it)
    • "Translator variables: 62"
    • "Translator facts: 133"
    • "Translator goal facts: 3"
    • "Translator operators: 358"
  • Search statistics:
    • "Expanded 85 state(s)."
    • "Reopened 0 state(s)."
    • "Evaluated 108 state(s)."
    • "Evaluations: 108"
    • "Generated 214 state(s)."
    • "Dead ends: 0 state(s)."
    • "Expanded until last jump: 77 state(s)."
    • "Reopened until last jump: 0 state(s)."
    • "Evaluated until last jump: 101 state(s)."
    • "Generated until last jump: 200 state(s)."
  • Others: Every type of planner has different relevant data to report and it is impossible for us to give a complete list of all the attributes that are relevant. The list above is based on the two most common kind of planners: portfolios and heuristic search planners (based on Fast Downward). However, this list is not closed. If your planner reports other useful statistics, or they are in a different format, we would like to parse them as well. Please, write us an email with a description of the information that your planner provides in the logs and, if possible, a Python regular expression that parses it.

Calls for Participation and Domains

Please forward the following calls to all interested parties.


Nine domains were used in the competition. A repository containing the PDDL files and the selected instances on Bitbucket.


4 planners + Optic as a base line participated in the temporal satisficing track. Planner abstracts for each planner are available as a planner abstract booklet or as individual files linked below.

The source code of all entries is publicly available on bitbucket. To build the planners, follow the instructions in the FAQ. Note that some planners require the LP solver CPLEX to compile. To build such a planner, acquire a CPLEX license, download the installation files to the directory /third-party or change that path in the Singularity file. Since building a singularity image requires root privileges, we recommend building the image inside a virtual machine. For example, you may use vagrant Vagrant with this Vagrantfile. It expects a directory ./input where the planner is located and an empty directory ./output where the image will be created.

Some planners had minor bug fixes after the competition. We have updated the last version of each planner.

Satisficing Track


The results were presented at the 28th International Conference on Automated Planning and Scheduling on June 29 in Delft. The presentation slides of this talk contain additional detail.

An overview of the scores is available online. Detailed results for all planners are available in two forms: a small repository per track contains an HTML table and a JSON file containing the parsed values for all instances (satisficing track, on-board planning track). If you require more detail about individual planner runs, the raw logs of all runs including all meta data generated by our scripts is available as well:

Even more detailed data that includes files generated by the planner during runtime are available on request.

Based on these results, we are proudly presenting the following awards.

Satisficing Track

  • Winner: TemPorAl
    by Isabel Cenamor, Mauro Vallati, Lukas Chrpa, Tomás de la Rosa and Fernando Fernández
  • Runner-Up: CP4TP
    by Daniel Furelos-Blanco and Anders Jonsson


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