This is a Java framework for Automated Planning, developed by Tomáš Balyo and Dominik Schreiber for the lecture Automated Planning and Scheduling at Karlsruhe Institute of Technology (KIT). It is meant as a simple, but extensible and reasonably powerful planning environment for PDDL problems, for educational and any other means.
Aquaplanning supports PDDL (Planning Domain Description Language) files as an input for planning domains and problems. The supported subset of PDDL is roughly on par with Fast Downward, today's prototypical state-of-the-art classical planner. This means that the following features are implemented as of now (i.e. problems with these features can be parsed, grounded, and solved):
- Basic STRIPS planning with typing
- Negative conditions (preconditions / effects / goals)
- Equality (as a universal predicate
(= obj1 obj2)) - Conditional effects (
when (condition) (additional-effect)) - Universal and existential quantifications
- All ADL features, i.e. disjunctive conditions with
or,imply, and non-atomicnotexpressions - Action costs (in its basic form
(:function total-cost - number), with constant positive cost per operator) - Derived predicates (as long as the resulting ground axioms are not directly recursing on themselves)
In addition, Aquaplanning supports the following aspects of numeric planning:
- Numeric fluents (a.k.a. functions), e.g.
(capacity ?truck) - Numeric expressions in preconditions, e.g.
(> (capacity ?truck) 0) - Numeric effects, e.g.
(decrease (capacity ?truck) 1)
For planning problems using these features (or any subset), a full representation of the read problem is available in the form of Java objects after parsing, as well as a separate ground representation after grounding the problem. For grounding purposes, Aquaplanning traverses the problem's (delete-)relaxed planning graph, resulting in a reasonable amount of atoms and actions in most cases.
A generic state space forward search is provided as a planner. As of now, a few common search strategies (BFS, DFS, A*, Weighted A*, Best-first, random choice) are implemented, as well as a couple of simple heuristics to guide the A* and Best-first searches.
At the end of the pipeline, a tiny plan validator can be employed to ensure the planner's correctness.
The framework is written from scratch and thus does not rely on any Planning-related frameworks. It does depend on antlr4 (for the parsing of PDDL files), Picocli for argument parsing, and sat4j as a SAT solver backend. We use Maven as a build system to resolve these dependencies (package maven in Debian-based Linux distributions).
In the base directory, run mvn package which will create a runnable jar file in target/aquaplanning-<version>-jar-with-dependencies.jar. You can launch the application with java -jar <runnable-jar>. JUnit tests are disabled by default (as they potentially take a long time); you can re-enable them by appending -DskipTests=false to the build command.
Aquaplanning can be used as an off-the-shelf planner; you can specify a domain file and a problem file as arguments (in that order), and it will attempt to parse, ground, and solve the problem. You can try the files provided in the testfiles/ directory.
All available options will be displayed when running the .jar without any arguments.
When you want to use your own planner, implement the Planner interface and take a look at the DefaultPlanner class as a point of reference.
If you find any bugs or you consider something to be missing, please let us know. We appreciate receiving issues and/or pull requests!