Symbolic Osp Planner Sym-Osp

Sym-Osp is a state-of-the-art optimal oversubscription planner based on symbolic search and extends Fast Downward.

Main source:

• Speck, D.; Michael Katz. 2021. Symbolic Search for Oversubscription Planning. In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021), AAAI Press. (to appear)

@InProceedings{speck-katz-aaai2021,
  author =       "David Speck and Michael Katz",
  title =        "Symbolic Search for Oversubscription Planning",
  booktitle =    "Proceedings of the Thirty-Fifth {AAAI} Conference on
                  Artificial Intelligence ({AAAI} 2021)",
  publisher =    "{AAAI} Press",
  year =         "2021",
  pages =        "11972--11980"
}

Dependencies

Currently we only support Linux systems. The following should install all necessary dependencies.

$ sudo apt-get -y install cmake g++ make python3 autoconf automake

Sym-Osp should compile on MacOS with the GNU C++ compiler and clang with the same instructions described above.

Compiling the Symbolic Osp Planner

$ ./build.py 

Osp Configurations

Sym-Osp uBDD representing the utility function as multiple BDDs to determine the utility values of sets of states.

$ ./fast-downward.py --translate --search domain.pddl problem.pddl --search "symosp-fw()"

Sym-Osp uADD representing the utility function as ADD to determine the utility values of sets of states.

$ ./fast-downward.py --translate --search domain.pddl problem.pddl --search "symosp-fw(use_add=true)"

Explicit A* search with the blind heuristic, representing the utility function as an ADD to determine the utility values of a single state.

$ ./fast-downward.py --translate --search domain.pddl problem.pddl --search "eager_osp(single(g()), f_eval=g(),reopen_closed=true)"

Benchmarks

Benchmarks for oversubscription planning can be found here.

Based on:

• Symbolic Top-k: https://github.com/speckdavid/symk
• Symbolic Fast Downward: https://fai.cs.uni-saarland.de/torralba/software.html
• Fast Downward: http://www.fast-downward.org/