Sym-Osp is a state-of-the-art optimal oversubscription planner based on symbolic search.
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",
note = "to appear"
}
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.
$ ./build.py
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)"
- 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/