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 multivalued 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 Downward’s approach to solving multi-valued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving

VHPOP is a partial order causal link (POCL) planner loosely based on UCPOP. It draws from the experience gained in the early to mid 1990’s on flaw selection strategies for POCL planning, and combines this with more recent developments in the field of domain independent planning such as distance based heuristics and reachability analysis. We present an adaptation of the additive heuristic for plan space planning, and modify it to account for possible reuse of existing actions in a plan. We also propose a large set of novel flaw selection strategies, and show how these can help us solve more problems than previously possible by POCL planners. VHPOP also supports planning with durative actions by incorporating standard techniques for temporal constraint reasoning. We demonstrate that the same heuristic techniques used to boost the performance of classical POCL planning can be effective in domains with durative actions as well. The result is a versatile heuristic POCL planner competitive with established CSP-based and heuristic state space planners.

During the last five years, the planning community has seen vast progress in terms of the sizes of benchmark examples that domain-independent planners can tackle successfully. The key technique behind this progress is the use of heuristic functions based on relaxing the planning task at hand, where the relaxation is to assume that all delete lists are empty. The success of such methods in many of the current benchmarks suggests that in those task's state spaces relaxed goal distances yield a heuristic function of high quality.

Fast-Forward, abbreviated FF, was the most successful automatic planner in the AIPS-2000 planning systems competition. Like the well known HSP system, FF relies on forward search in the state space, guided by a heuristic that estimates goal distances by ignoring delete lists. It differs from HSP in a number of important details. This article describes the algorithmic techniques used in FF in comparison to HSP, and evaluates their benefits in terms of runtime and solution length behavior.

This paper presents GRT, a domain-independent heuristic planning system for STRIPS worlds. GRT solves

. The efficiency of AI planning systems is usually evaluated empirically. The planning domains used in the competitions of the 1998 and 2000 AIPS conferences are of particular importance in this context. Many of these domains share a common theme of transporting portables, making use of mobiles traversing a map of locations and roads. In this contribution, we embed these benchmarks into a well-structured hierarchy of transportation problems and study the computational complexity of optimal and non-optimal planning in this domain family. We identify the key features that make transportation tasks hard and try to shed some light on the recent success of planning systems based on heuristic local search, as observed in the AIPS 2000 competition. 1

This paper describes Marvin, a planner that competed in the Fourth International Planning Competition (IPC 4). Marvin uses action-sequence-memoisation techniques to generate macroactions, which are then used during search for a solution plan. We provide an overview of its architecture and search behaviour, detailing the algorithms used. We also empirically demonstrate the effectiveness of its features in various planning domains; in particular, the effects on performance due to the use of macro-actions, the novel features of its search behaviour, and the native support of ADL and Derived Predicates. 1.

In this paper we combine the goal directed search of A* with the ability of BDDs to traverse an exponential number of states in polynomial time. We introduce a new algorithm, SetA*, that generalizes A* to expand sets of states in each iteration. SetA* has substantial advantages over BDDA*, the only previous BDD-based A* implementation we are aware of. Our experimental evaluation proves SetA* to be a powerful search paradigm. For some of the studied problems it outperforms BDDA*, A*, and BDD-based breadth-first search by several orders of magnitude. We believe exploring sets of states to be essential when the heuristic function is weak. For problems with strong heuristics, SetA* efficiently specializes to single-state search and consequently challenges single-state heuristic search in general.

Despite major progress in AI planning over the last few years, many interesting domains remain challenging for current planners. This paper presents component abstraction, an automatic and generic technique that can reduce the complexity of an important class of planning problems. Component abstraction uses static facts in a problem definition to decompose the problem into linked abstract components. A local analysis of each component is performed to speed up planning at the component level. Our implementation uses this analysis to statically build macro operators specific to each component. A dynamic filtering process keeps for future use only the most useful macro operators. We demonstrate our ideas in Depots, Satellite, and Rovers, three standard domains used in the third AI planning competition. Our results show an impressive potential for macro operators to reduce the search complexity and achieve more stable performance.

Recent trends in planning research have led to empirical comparison becoming commonplace. The field has started to settle...