Powerlifted Planner

Powerlifted is a domain-independent classical planner that uses only lifted representations.

(See References for more details.)

If you want to access the version used in the paper Landmark Heuristics for Lifted Classical Planning by Wichlacz et al. IJCAI 2022 please use the tag wichlacz-et-al-ijcai2022 (https://github.com/minecraft-saar/powerlifted/releases/tag/wichlacz-et-al-ijcai2022)

Usage

The powerlifted.py script solves a PDDL task provided as input. It also builds the planner if the --build parameter is passed. The script has the following parameters:

$ ./powerlifted.py [-d DOMAIN] -i INSTANCE -s SEARCH -e HEURISTIC -g GENERATOR [--state STATE REPR.] [ADDITIONAL OPTIONS] [--build]

Use the build.py script to build the planner first.

Best Configuration for Satisficing Planning

Currently, the best configuration for satisficing planning (with respect to total coverage) is the following:

$ ./powerlifted.py [-d DOMAIN] -i INSTANCE -s alt-bfws1 -e ff -g yannakakis [ADDITIONAL OPTIONS] [--build]

These are also the default values for -s, -e, and -g. To maximize coverage, we also recommend adding --unit-cost (see below) to the ADDITIONAL OPTIONS.

Available Options for SEARCH:

• alt-bfws1 and alt-bfws2: [R_x, h] with w=1 and w=2, respectively. The choice of h is given the HEURISTIC option. (See Corrêa and Seipp 2022.)
• astar: A* Search
• bfs: Breadth-First Search (This option was previously called naive. You can still use naive with the powerlifted.py script but the planner will internally use the new keyword bfs.)
• bfws1 and bfws2: Best-First Width Search with w=1 and w=2, respectively.
• bfws1-rx and bfws2-rx: BFWS(R_x) with w=1 and w=2, respectively. (See Corrêa and Seipp 2022.)
• dq-bfws1-rx and dq-bfws2-rx: DQ(R_x) with w=1 and w=2, respectively. (See Corrêa and Seipp 2022.)
• gbfs: Greedy Best-First Search
• iw1 and iw2: Iterated Width Search (with w=1 and w=2, respectively)
• lazy: Lazy Best-First Search
• lazy-po: Lazy Best-First Search with Boosted Dual-Queue
• lazy-prune: Lazy Best-First Search with pruning of states generated by non-preferred operators

Available Options for HEURISTIC:

• add: The additive heuristic
• blind: No Heuristic
• ff: The FF heuristic
• goalcount: The goal-count/STRIPS heuristic
• hmax: The hmax heuristic
• rff: The rule-based FF heuristic

Available Options for GENERATOR:

• join: Join program using the predicate order given in the PDDL file
• random_join: Randomly ordered join program
• ordered_join: Join program ordered by the arity of the predicates
• full_reducer: Generate successor for acyclic schemas using the full reducer method; for cyclic schemas it uses a partial reducer and a join program.
• yannakakis: Same as above but replaces the final join of the full reducer method by the Yannakakis' project-join program.

Available Options for STATE REPR.:

• sparse: Use the sparse state representation where a state is only represented by the facts that are true in this state.
• extensional: Use the extensional representation where a state is a bitset where the ith-bit is true if the fact associated to it is true in this state. This representation requires the grounding of facts (but not of actions) which, right now, is performed in the search component. Warning: this options does not support all HEURISTIC options.

Available ADDITIONAL OPTIONS:

• [--only-effects-novelty-check]: Flag if the novelty evaluation of a state should only consider atoms in the applied action effect. Warning: for state-of-the-art performance, you must use this option when running BFWS-based search engines. (See Corrêa and Seipp 2022.)
• [--novelty-early-stop]: Flag if the novelty evaluation of a state should stop as soon as the return value is defined. (See Corrêa and Seipp 2022.)
• [--seed RANDOM SEED]: Random seed for the random number generator.
• [--translator-output-file TRANSLATOR_FILE]: Output of the intermediate representation to be parsed by the search component will be saved into TRANSLATOR_FILE. (Default: output.lifted)
• [--unit-cost]: Use unit cost (i.e., all costs are equal to 1) instead of the costs specified in the domain file.
• [--validate]: Runs VAL after a plan is found to validate it. This requires VAL to be added as validate to the PATH.

Running Powerlifted as a Singularity container

You can also build a Singularity image to run the planner. This might be useful in the case where you are not able to compile the planner locally, for example. To do so, first remove the builds/ directory, in case you have any builds already in your system. Then, you can run the following command to create the planner image:

sudo singularity build powerlifted.sif Singularity

Be aware that this might take a while. Once the image powerlifted.sif is created, you can run it with the same parameters as the powerlifted.py script. The only exception is that, by default, VAL is not installed in the container, so it is not possible to use the --validate flag with the Singularity image. However, you can run VAL with the sas_plan file created by the planner after the execution. The following command is a usage example on how to run the planner with the Singularity image:

./powerlifted.sif -i /path/to/instance.pddl -s lazy-po -e add -g yannakakis --datalog-file model.lp --translator-output-file output.lifted

Requirements

• A C++17-compliant compiler
• CMake 3.9+
• Python 3.5+
• Boost

The planner also uses data-structures from Abseil. The source code of Abseil is contained in the src/search/abseil-cpp directory

Limitations

• Axioms: not supported
• Conditional effects: not supported
• Negated preconditions: only inequality
• Quantifiers: not supported

References

1. Corrêa, A. B.; Pommerening, F.; Helmert, M.; and Francès, G. 2020. Lifted Successor Generation using Query Optimization Techniques. In Proc. ICAPS 2020, pp. 80-89. [pdf]
2. Corrêa, A. B.; Francès, G.; Pommerening, F.; and Helmert, M. 2021. Delete-Relaxation Heuristics for Lifted Classical Planning. In Proc. ICAPS 2021, pp. 94-102. [pdf]
3. Corrêa, A. B.; Pommerening, F.; Helmert, M.; and Francès, G. 2022. The FF Heuristic for Lifted Classical Planning. In Proc. AAAI 2022. [pdf]
4. Corrêa, A. B.; and Seipp, J. 2022. Best-First Width Search for Lifted Classical Planning. In Proc. ICAPS 2022. [pdf]