I actively enjoy encoding knowledge planning models, usually in PDDL or PDDL+.
Some of the models I engineered have been used in International Competitions, while others have been used for testing the exploitability of planning in some real-world applications. I also modified well-known benchmark models to test the robustness of state-of-the-art domain-independent planning engines.
All the models can be found in a bitbucket repository I specifically designed, https://bitbucket.org/maurovallati/planning-knowledge-models
Some example models we developed for using PDDL+ planning for in-station train dispatching can be found here: https://github.com/matteocarde/icaps2021
Macros and learning tool from the "Critical Section Macros" approach: https://github.com/lchrpa/CSMs/tree/main
The deployable PDDL+ models for urban traffic control, with small example problems, can be found here: https://github.com/anas-elkouaiti/utc-models-deployable
Feel free to use the models for your research, or just to play a bit with planning engines.
PbP, a planning system based on an automatically configurable portfolio of planners (with Alfonso Gerevini and Alessandro Saetti); Winner of the learning track of the 6th International Planning Competition (2008). PbP2, an improved version, was the winner of the learning track of the 7th International Planning Competition (2011).
MacroSatPlan, SAT-based optimal planner which exploits a predictive model and macros to speedup the SAT solving (with Alfonso Gerevini and Alessandro Saetti).
ParLPG, a planning system based on the idea of automatically configuring a generic,parameterized planner: LPG. (with Alfonso Gerevini, Alessandro Saetti, Chris Fawcett and Holger Hoos).
TemPoRal, a portfolio-based temporal planning system developed (in fact, mainly by Isabel Cenamor) for the IPC 2018. It was also the winner, btw.
ArgSemSAT, developed with Federico Cerutti, is a novel SAT-based approach for solving various tasks in abstract argumentation. It was the runner-up of the 1st International Competition on Computational Models of Argumentation (ICCMA), and the winner of the PR track of the 2nd International Competition on Computational Models of Argumentation (ICCMA).
fudge, developed with Matthias Thimm and Federico Cerutti, is a lightweight yet extremely performant SAT-based solver for various tasks in abstract argumentation. It was the winner of the ideal track of ICCMA 2021.
JArgSemSAT, is a portable java-based version of ArgSemSAT.
AFBenchGen, developed with Federico Cerutti and Massimiliano Giacomin, is a highly configurable generator of challenging Abstract Argumentation frameworks. It is described in this paper.