This is the main software repository of the Artificial Intelligence and Machine Learning Research Group at Universitat Pompeu Fabra, Barcelona, Spain. You can browse the full list of publicly available projects on the group’s Github page, or click directly to a particular project on the list below:
The Automated Programming Framework includes the code necessary to configure and execute different compilations related to the formalisms of Planning Programs and Hierarchical Finite State Controllers. These compilations are produced from the original planning problems using the Universal PDDL Parser, and solved using classical planners which are included in the repository.
- Javier Segovia-Aguas, Sergio Jiménez and Anders Jonsson (2017). Unsupervised Classification of Planning Instances. Proceedings of the 27th International Conference on Automated Planning and Scheduling.
- Damir Lotinac, Javier Segovia-Aguas, Sergio Jiménez and Anders Jonsson (2016). Automatic Generation of High-Level State Features for Generalized Planning. Proceedings of the 25th International Joint Conference on Artificial Intelligence.
- Javier Segovia-Aguas, Sergio Jiménez and Anders Jonsson (2016). Hierarchical Finite State Controllers for Generalized Planning. Proceedings of the 25th International Joint Conference on Artificial Intelligence.
- Javier Segovia-Aguas, Sergio Jiménez and Anders Jonsson (2016). Generalized Planning With Procedural Domain Control Knowledge. Proceedings of the 26th International Conference on Automated Planning and Scheduling.
FS is a classical planner based on the Functional STRIPS planning language, with additional support for existential quantification, state constraints and a fairly large library of global constraints which are useful both from the expressive and computational points of view.
- Francès, G., and Geffner, H. (2015),Modeling and Computation in Planning: Better Heuristics from More Expressive Languages, ICAPS 2015.
- Francès, G., and Geffner, H. (2016a), E-STRIPS: Existential Quantification in Planning and Constraint Satisfaction, IJCAI 2016.
- Francès, G., and Geffner, H. (2016b), Effective Planning with More Expressive Languages, IJCAI 2016.
- Geffner, H. (2000), Functional STRIPS: A more flexible language for planning and problem solving. In Minker, J., ed., Logic-Based Artificial Intelligence. Kluwer. 187–205.
LAPKT is an open-source framework written in
Python to ease the creation of high-performance automated planners.
The toolkit has been succesfully used as the basis for a number of planners from
the last International Planning Competition (2014), as well as
in several research projects on compilation-based and replanning approaches.
The full documentation of the toolkit can be found on the project’s main website.
PROBE is a classical planner developed by Nir Lipovetzky and Hector Geffner that participated in the seventh International Planning Competition (2011) with remarkable performance. See “Searching for plans with carefully designed probes”, N. Lipovetzky and H. Geffner. In Proceedings ICAPS 2011.
- N. Lipovetzky and H. Geffner (2011). Searching for plans with carefully designed probes. ICAPS 2011.
PDDL parser written in
C++ and, used in several of the other software projects within the group.
Provides support for programmatically parsing and generating
Extends the Universal PDDL Parser to provide support for parsing multiagent planning instances expressed in
Currently also includes a solver for concurrent multiagent planning in which agents can take actions in parallel;
the solver is based on a compilation from multiagent planning to classical planning.
- Matthew Crosby, Anders Jonsson and Michael Rovatsos (2014). A Single-Agent Approach to Multiagent Planning. Proceedings of the 21st European Conference on Artificial Intelligence.