Revision 20 as of 2010-12-07 07:46:42

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Usage

Running the planner is a three-step process as explained in Section 3 (pp. 202-203) of the JAIR paper on Fast Downward. The following instructions show how to run these three steps, in sequence, assuming that the preprocessor and search component have been compiled and that you are currently located in the src directory.

Translator

translate/translate.py [DOMAIN] PROBLEM

If the domain file is not given, the planner will try to infer a likely name from the problem file name, using the conventions used at the various IPCs. (If in doubt if this will work for you, just try it out.)

Note: Creates a file called output.sas (as well as test.groups, all.groups, ...)

Preprocessor

preprocess/preprocess < OUTPUT.SAS

Note: Creates a file called output

Search component

search/downward [OPTIONS] --search SEARCH < OUTPUT

Options:

Examples

A* search:

   1 # landmark-cut heuristic (previously configuration "ou")
   2  ./downward --search "astar(lmcut())" < output
   3 
   4 # merge-and-shrink heuristic with default settings (previously configuration "oa50000")
   5  ./downward --search "astar(mas())" < output
   6 
   7 # blind heuristic (previously configuarion "ob")
   8  ./downward --search "astar(blind())" < output

Lazy greedy best first search with preferred operators and the queue alternation method:

   1 ## using FF heuristic and context-enhanced additive heuristic (previously: "fFyY")
   2  ./downward --heuristic "hff=ff()" --heuristic "hcea=cea()" \
   3             --search "lazy_greedy(hff, hcea, preferred=(hff, hcea))" \
   4             < output
   5 
   6 ## using FF heuristic (previously: "fF")
   7  ./downward --heuristic "hff=ff()" \
   8             --search "lazy_greedy(hff, preferred=(hff))" \
   9             < output
  10 
  11 ## using context-enhanced additive heuristic (previously: "yY")
  12  ./downward --heuristic "hcea=cea()" \
  13             --search "lazy_greedy(hcea, preferred=(hcea))" \
  14             < output

The above examples use the new best-first search implementation. For comparison, the old best-first search implementation is still available:

 ./downward --heuristic "hff=ff()" --heuristic "hcea=cea()" \
            --search "old_greedy(hff, hcea, preferred=(hff, hcea))" \
            < output

Q: I would like to see an example that uses the Lama-FF Synergy feature. I'd be most interested in a configuration that is as close to LAMA as currently possible (i.e., using the LAMA and FF heuristics with synergy in a lazy alternation search with preferred operators for both heuristics, using iterated search with the appropriate set of options).

A: This is as close to LAMA as we have right now (same search algorithms and heuristics as in LAMA, iterated search, but no caching and no +1 to operator cost). The first search iteration uses the lazy greedy alternation search with LAMA and FF heuristics with synergy, using both heuristics for preferred operators. Later search iterations are conducted with the same heuristics and preferred operators, but using weighted A* search with a weight schedule rather than greedy search.

./downward --heuristic "hlm,hff=lm_ff_syn(lm_rhw())"
           --search "iterated(
                              lazy_greedy(hff,hlm,preferred=(hff,hlm)),
                              lazy_wastar(hff,hlm,preferred=(hff,hlm),w=5),
                              lazy_wastar(hff,hlm,preferred=(hff,hlm),w=3),
                              lazy_wastar(hff,hlm,preferred=(hff,hlm),w=2),
                              lazy_wastar(hff,hlm,preferred=(hff,hlm),w=1),
                              repeat_last=true)" < output

The following is the corresponding call to just find a first solution (i.e., not doing iterated search):

 ./downward --heuristic "hlm,hff=lm_ff_syn(lm_rhw())" 
            --search "lazy_greedy(hlm, hff, preferred=(hlm, hff))" 
            < output

If you would like to have another translation from an old-style configuration to the new call-syntax, please add it here as a TODO.