Fast Downward is a classical planning system based on heuristic search. It
can deal with general deterministic planning problems encoded in the
propositional fragment of PDDL2.2, including advanced features like ADL
conditions and effects and derived predicates (axioms). Like other well-known
planners such as HSP and FF, Fast Downward is a progression planner, searching
the space of world states of a planning task in the forward direction. However,
unlike other PDDL planning systems, Fast Downward does not use the
propositional PDDL representation of a planning task directly. Instead, the
input is first translated into an alternative representation called
multi-valued planning tasks, which makes many of the implicit constraints of a
propositional planning task explicit. Exploiting this alternative
representation, Fast Downward uses hierarchical decompositions of planning
tasks for computing its heuristic function, called the causal graph heuristic,
which is very different from traditional HSP-like heuristics based on ignoring
negative interactions of operators.
In this article, we give a full account of Fast Downwards approach to solving
multi-valued planning tasks. We extend our earlier discussion of the causal
graph heuristic to tasks involving axioms and conditional effects and present
some novel techniques for search control that are used within Fast Downwards
best-first search algorithm: preferred operators transfer the idea of helpful
actions from local search to global best-first search, deferred evaluation of
heuristic functions mitigates the negative effect of large branching factors on
search performance, and multi-heuristic best-first search combines several
heuristic evaluation functions within a single search algorithm in an
orthogonal way. We also describe efficient data structures for fast state
expansion (successor generators and axiom evaluators) and present a new
non-heuristic search algorithm called focused iterative-broadening search,
which utilizes the information encoded in causal graphs in a novel way.
Fast Downward has proven remarkably successful: It won the "classical (i.e.,
propositional, non-optimising) track of the 4th International Planning
Competition at ICAPS 2004, following in the footsteps of planners such as FF
and LPG. Our experiments show that it also performs very well on the benchmarks
of the earlier planning competitions and provide some insights about the
usefulness of the new search enhancements.

Fast Downward 是一种基于启发式搜索的经典计划系统，它使用多值计划任务的另一种表示来计算其启发函数，称为因果图启发式函数，这与传统的基于忽略运算符负交互作用的 HSP 启发式函数非常不同。