Some AI planners that work with PDDL and examples.

Contingent-FF is an extension of Conformant-FF to further treat partial observability (observation actions), finding tree-shaped plans with branches; the system also includes a preliminary treatment of a simple form of non-deterministic effects. The planner does not support negative preconditions. While Conformant-FF planner is a system that extends the classical FF planner with the ability to treat initial state uncertainty expressed in the form of a CNF formula.

This binary is the one provided by ROSPlan but the planner was developed by J. Hoffmann in collaboration with R. Brafman.

Usage: ./Contingent-FF -o <domain file> -f <problem file>

References:

• https://fai.cs.uni-saarland.de/hoffmann/cff.html
• https://github.com/KCL-Planning/ROSPlan

FF is a very successful forward-chaining heuristic search planner producing sequential plans.

Usage: ./ff -o <domain file> -f <problem file>

References:

• https://github.com/guillaume-chevalier/AI-Planning-Solver-Shakeys-World-PDDL
• https://www.ida.liu.se/%7ETDDC17/info/labs/lab4_planning.en.shtml

POPF name derives from the fact that it incorporates ideas from partial-order planning, it does not support negative preconditions. This binary is the one provided by ROSPlan, but the planner was developed by A. J. Coles, A. I. Coles, M. Fox, and D. Long_.

Usage: ./popf <domain file> <problem file>

References:

• https://nms.kcl.ac.uk/planning/software/popf.html
• https://github.com/KCL-Planning/ROSPlan

IPP is a descendant of Graphplan and it produces optimal parallel plans.

Usage: ./ipp -o <domain file> -f <problem file>

References:

• https://github.com/guillaume-chevalier/AI-Planning-Solver-Shakeys-World-PDDL
• https://www.ida.liu.se/%7ETDDC17/info/labs/lab4_planning.en.shtml