fast-downward tutorial

[José María Bernad]

Hello,

I am studying automated planning subject as part of the AI masters degree I am currently finishing. I came to fast downward as this is the tool I am told to use for one of the asignments of this subject.

The assignment asks me to run fastdownward this way:

./fast-downward.py domain.pddl problem.pddl --evaluator "hff=ff()" --search "lazy_greedy([hff],preferred=[hff])"

Which seems to create a far from optimal plan for the problem I am trying to solve. This is more than enough for solving my assignment but I think it is useless for solving real world problems.

Could you please provide some instructions on how to use the fast-downward planner to find optimal plans?

Is this fast-downward planner suitable for solving real-world problems? Or are there better options for automatic planning?

Thank you for your response in advance and kind regards

Jose Mari

[Florian Pommerening]

Hi Jose Mari,

there are configurations of Fast Downward that are guaranteed to produce
optimal plans. In general, you can use A* together with any admissible
heuristic (see https://www.fast-downward.org/Doc/Evaluator to find out
which of the heuristics are admissible). A good starting point is A*
with LM-cut:

./fast-downward.py domain.pddl problem.pddl --search "astar(lmcut())"

Finding optimal plans is often much more difficult than finding any
plan, so this configuration will not scale to the same size of problem
that the FF configuration will. That is why in practice, you will
probably see more satisficing planners (= not guaranteeing optimality).
There is a possible middle ground with configurations like weighted A*,
that limit the amount of suboptimality or anytime solvers like LAMA
('./fast-downward.py --alias lama domain.pddl problem.pddl') that use
the available time to find better and better solutions.

Cheers
Florian

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[José María Bernad]

Thank you all for your comments. :-)