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66
The Fast Downward planning system
 Journal of Artifical Intelligence Research
, 2006
"... 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 wellknown planne ..."
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Cited by 345 (29 self)
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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 wellknown 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 HSPlike heuristics based on ignoring negative interactions of operators. In this article, we give a full account of Fast Downward’s approach to solving multivalued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving
Landmarks, Critical Paths and Abstractions: What’s the Difference Anyway?
, 2009
"... Current heuristic estimators for classical domainindependent planning are usually based on one of four ideas: delete relaxations, critical paths, abstractions, and, most recently, landmarks. Previously, these different ideas for deriving heuristic functions were largely unconnected. We prove that a ..."
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Cited by 111 (28 self)
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Current heuristic estimators for classical domainindependent planning are usually based on one of four ideas: delete relaxations, critical paths, abstractions, and, most recently, landmarks. Previously, these different ideas for deriving heuristic functions were largely unconnected. We prove that admissible heuristics based on these ideas are in fact very closely related. Exploiting this relationship, we introduce a new admissible heuristic called the landmark cut heuristic, which compares favourably with the state of the art in terms of heuristic accuracy and overall performance.
The deterministic part of IPC4: an overview
, 2005
"... We provide an overview of the organization and results of the deterministic part of the 4th International Planning Competition, i.e., of the part concerned with evaluating systems doing deterministic planning. IPC4 attracted even more competing systems than its already large predecessors, and the c ..."
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Cited by 75 (10 self)
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We provide an overview of the organization and results of the deterministic part of the 4th International Planning Competition, i.e., of the part concerned with evaluating systems doing deterministic planning. IPC4 attracted even more competing systems than its already large predecessors, and the competition event was revised in several important respects. After giving an introduction to the IPC, we briefly explain the main differences between the deterministic part of IPC4 and its predecessors. We then introduce formally the language used, called PDDL2.2 that extends PDDL2.1 by derived predicates and timed initial literals. We list the competing systems and overview the results of the competition. The entire set of data is far too large to be presented in full. We provide a detailed summary; the complete data is available in an online appendix. We explain how we awarded the competition prizes.
Concise finitedomain representations for PDDL planning tasks
, 2009
"... We introduce an efficient method for translating planning tasks specified in the standard PDDL formalism into a concise grounded representation that uses finitedomain state variables instead of the straightforward propositional encoding. Translation is performed in four stages. Firstly, we transfo ..."
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Cited by 62 (13 self)
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We introduce an efficient method for translating planning tasks specified in the standard PDDL formalism into a concise grounded representation that uses finitedomain state variables instead of the straightforward propositional encoding. Translation is performed in four stages. Firstly, we transform the input task into an equivalent normal form expressed in a restricted fragment of PDDL. Secondly, we synthesize invariants of the planning task that identify groups of mutually exclusive propositions which can be represented by a single finitedomain variable. Thirdly, we perform an efficient relaxed reachability analysis using logic programming techniques to obtain a grounded representation of the input. Finally, we combine the results of the third and fourth stage to generate the final grounded finitedomain representation. The presented approach has originally been implemented as part of the Fast Downward planning system for the 4th International Planning Competition (IPC4). Since then, it has been used in a number of other contexts with considerable success, and the use of concise finitedomain representations has become a common feature of stateoftheart planners.
A heuristic search planner with online macroaction learning
"... This paper describes Marvin, a planner that competed in the Fourth International Planning Competition (IPC 4). Marvin uses actionsequencememoisation techniques to generate macroactions, which are then used during search for a solution plan. We provide an overview of its architecture and search be ..."
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Cited by 36 (3 self)
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This paper describes Marvin, a planner that competed in the Fourth International Planning Competition (IPC 4). Marvin uses actionsequencememoisation 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 macroactions, the novel features of its search behaviour, and the native support of ADL and Derived Predicates. 1.
Strengthening Landmark Heuristics via Hitting Sets
"... The landmark cut heuristic is perhaps the strongest known polytime admissible approximation of the optimal delete relaxation heuristic h +. Equipped with this heuristic, a bestfirst search was able to optimally solve 40 % more benchmark problems than the winners of the sequential optimization track ..."
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Cited by 21 (5 self)
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The landmark cut heuristic is perhaps the strongest known polytime admissible approximation of the optimal delete relaxation heuristic h +. Equipped with this heuristic, a bestfirst search was able to optimally solve 40 % more benchmark problems than the winners of the sequential optimization track of IPC 2008. We show that this heuristic can be understood as a simple relaxation of a hitting set problem, and that stronger heuristics can be obtained by considering stronger relaxations. Based on these findings, we propose a simple polytime method for obtaining heuristics stronger than landmark cut, and evaluate them over benchmark problems. We also show that hitting sets can be used to characterize h + and thus provide a fresh and novel insight for better comprehension of the delete relaxation. 1
An LPbased heuristic for optimal planning
 In International Conference on Principles and Practice of Constraint Programming (CP
, 2007
"... Abstract. One of the most successful approaches in automated planning is to use heuristic statespace search. A popular heuristic that is used by a number of statespace planners is based on relaxing the planning task by ignoring the delete effects of the actions. In several planning domains, howe ..."
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Cited by 20 (8 self)
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Abstract. One of the most successful approaches in automated planning is to use heuristic statespace search. A popular heuristic that is used by a number of statespace planners is based on relaxing the planning task by ignoring the delete effects of the actions. In several planning domains, however, this relaxation produces rather weak estimates to guide search effectively. We present a relaxation using (integer) linear programming that respects delete effects but ignores action ordering, which in a number of problems provides better distance estimates. Moreover, our approach can be used as an admissible heuristic for optimal planning.
Analyzing search topology without running any search: On the connection between causal graphs and h+
 JAIR
, 2011
"... The ignoring delete lists relaxation is of paramount importance for both satisficing and optimal planning. In earlier work, it was observed that the optimal relaxation heuristic h + has amazing qualities in many classical planning benchmarks, in particular pertaining to the complete absence of local ..."
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Cited by 19 (3 self)
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The ignoring delete lists relaxation is of paramount importance for both satisficing and optimal planning. In earlier work, it was observed that the optimal relaxation heuristic h + has amazing qualities in many classical planning benchmarks, in particular pertaining to the complete absence of local minima. The proofs of this are handmade, raising the question whether such proofs can be lead automatically by domain analysis techniques. In contrast to earlier disappointing results – the analysis method has exponential runtime and succeeds only in two extremely simple benchmark domains – we herein answer this question in the affirmative. We establish connections between causal graph structure and h + topology. This results in loworder polynomial time analysis methods, implemented in a tool we call TorchLight. Of the 12 domains where the absence of local minima has been proved, TorchLight gives strong success guarantees in 8 domains. Empirically, its analysis exhibits strong performance in a further 2 of these domains, plus in 4 more domains where local minima may exist but are rare. In this way, TorchLight can distinguish “easy” domains from “hard” ones. By summarizing structural reasons for analysis failure, TorchLight also provides diagnostic output indicating domain aspects that may cause local minima.
A New LocalSearch Algorithm for ForwardChaining Planning
 In Proceedings of ICAPS 07
, 2007
"... Forwardchaining heuristic search is a wellestablished and popular paradigm for domainindependent planning. Its effectiveness relies on the heuristic information provided by a state evaluator, and the search algorithm used with this in order to solve the problem. This paper presents a new stocha ..."
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Cited by 17 (1 self)
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Forwardchaining heuristic search is a wellestablished and popular paradigm for domainindependent planning. Its effectiveness relies on the heuristic information provided by a state evaluator, and the search algorithm used with this in order to solve the problem. This paper presents a new stochastic localsearch algorithm for forwardchaining planning. The algorithm is used as the basis of a planner in conjunction with FF’s Relaxed Planning Graph heuristic. Our approach is unique in that localised restarts are used, returning to the start of plateaux and saddle points, as well as global restarts to the initial state. The majority of the search time when using FF’s ‘Enforced Hill Climbing ’ is spent using breadthfirst search to escape local minima. Our localised restarts, in conjunction with stochastic search, serve to replace this expensive breadthfirst search step. We also describe an extended search neighbourhood incorporating nonhelpful actions and the ‘lookahead ’ states used in YAHSP. Making use of nonhelpful actions and stochastic search allows us to restart the localsearch from the initial state when deadends are encountered; rather than resorting to bestfirst search. We present analyses to demonstrate the effectiveness of our restart strategies, along with results that show the new planning algorithm is effective across a range of domains.
Learning relational decision trees for guiding heuristic planning
 In Proceedings of the 18th International Conference on Automated Planning and Scheduling (ICAPS 08
, 2008
"... The current evaluation functions for heuristic planning are expensive to compute. In numerous domains these functions give good guidance on the solution, so it worths the computation effort. On the contrary, where this is not true, heuristics planners compute loads of useless node evaluations that m ..."
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Cited by 16 (5 self)
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The current evaluation functions for heuristic planning are expensive to compute. In numerous domains these functions give good guidance on the solution, so it worths the computation effort. On the contrary, where this is not true, heuristics planners compute loads of useless node evaluations that make them scaleup poorly. In this paper we present a novel approach for boosting the scalability of heuristic planners based on automatically learning domainspecific search control knowledge in the form of relational decision trees. Particularly, we define the learning of planning search control as a standard classification process. Then, we use an offtheshelf relational classifier to build domainspecific relational decision trees that capture the preferred action in the different planning contexts of a planning domain. These contexts are defined by the set of helpful actions extracted from the relaxed planning graph of a given state, the goals remaining to be achieved, and the static predicates of the planning task. Additionally, we show two methods for guiding the search of a heuristic planner with relational decision trees. The first one consists of using the resulting decision trees as an action policy. The second one consists of ordering the node evaluation of the Enforced Hill Climbing algorithm with the learned decision trees. Experiments over a variety of domains from the IPC testbenchmarks reveal that in both cases the use of the learned decision trees increase the number of problems solved together with a reduction of the time spent.