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830
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 well-known planne ..."
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Cited by 347 (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 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
The Metric-FF planning system: Translating ”ignoring delete lists” to numeric state variables.
- Journal Artificial Intelligence Research (JAIR)
, 2003
"... Abstract Planning with numeric state variables has been a challenge for many years, and was a part of the 3rd International Planning Competition (IPC-3). Currently one of the most popular and successful algorithmic techniques in STRIPS planning is to guide search by a heuristic function, where the ..."
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Cited by 179 (12 self)
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Abstract Planning with numeric state variables has been a challenge for many years, and was a part of the 3rd International Planning Competition (IPC-3). Currently one of the most popular and successful algorithmic techniques in STRIPS planning is to guide search by a heuristic function, where the heuristic is based on relaxing the planning task by ignoring the delete lists of the available actions. We present a natural extension of "ignoring delete lists" to numeric state variables, preserving the relevant theoretical properties of the STRIPS relaxation under the condition that the numeric task at hand is "monotonic". We then identify a subset of the numeric IPC-3 competition language, "linear tasks", where monotonicity can be achieved by preprocessing. Based on that, we extend the algorithms used in the heuristic planning system FF to linear tasks. The resulting system Metric-FF is, according to the IPC-3 results which we discuss, one of the two currently most efficient numeric planners.
Planning through Stochastic Local Search and Action Graphs in LPG
- Journal of Artificial Intelligence Research (JAIR
, 1996
"... We present some techniques for planning in domains specified with the recent standard language pddl2.1, supporting “durative actions ” and numerical quantities. These tech-niques are implemented in lpg, a domain-independent planner that took part in the 3rd International Planning Competition (IPC). ..."
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Cited by 169 (22 self)
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We present some techniques for planning in domains specified with the recent standard language pddl2.1, supporting “durative actions ” and numerical quantities. These tech-niques are implemented in lpg, a domain-independent planner that took part in the 3rd International Planning Competition (IPC). lpg is an incremental, any time system pro-ducing multi-criteria quality plans. The core of the system is based on a stochastic local search method and on a graph-based representation called “Temporal Action Graphs ” (TA-graphs). This paper focuses on temporal planning, introducing TA-graphs and proposing some techniques to guide the search in lpg using this representation. The experimental results of the 3rd IPC, as well as further results presented in this paper, show that our techniques can be very effective. Often lpg outperforms all other fully-automated plan-ners of the 3rd IPC in terms of speed to derive a solution, or quality of the solutions that can be produced. 1.
The 3rd international planning competition: Results and analysis
- Journal of Artificial Intelligence Research
, 2003
"... This paper reports the outcome of the third in the series of biennial international planning competitions, held in association with the International Conference on AI Planning and Scheduling (AIPS) in 2002. In addition to describing the domains, the planners and the objectives of the competition, th ..."
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Cited by 146 (20 self)
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This paper reports the outcome of the third in the series of biennial international planning competitions, held in association with the International Conference on AI Planning and Scheduling (AIPS) in 2002. In addition to describing the domains, the planners and the objectives of the competition, the paper includes analysis of the results. The results are analysed from several perspectives, in order to address the questions of comparative performance between planners, comparative difficulty of domains, the degree of agreement between planners about the relative difficulty of individual problem instances and the question of how well planners scale relative to one another over increasingly difficult problems. The paper addresses these questions through statistical analysis of the raw results of the competition, in order to determine which results can be considered to be adequately supported by the data. The paper concludes with a discussion of some challenges for the future of the competition series. 1.
The LAMA planner: guiding cost-based anytime planning with landmarks.
- Journal Artificial Intelligence Research (JAIR)
, 2010
"... Abstract LAMA is a classical planning system based on heuristic forward search. Its core feature is the use of a pseudo-heuristic derived from landmarks, propositional formulas that must be true in every solution of a planning task. LAMA builds on the Fast Downward planning system, using finite-dom ..."
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Cited by 141 (5 self)
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Abstract LAMA is a classical planning system based on heuristic forward search. Its core feature is the use of a pseudo-heuristic derived from landmarks, propositional formulas that must be true in every solution of a planning task. LAMA builds on the Fast Downward planning system, using finite-domain rather than binary state variables and multi-heuristic search. The latter is employed to combine the landmark heuristic with a variant of the well-known FF heuristic. Both heuristics are cost-sensitive, focusing on high-quality solutions in the case where actions have non-uniform cost. A weighted A * search is used with iteratively decreasing weights, so that the planner continues to search for plans of better quality until the search is terminated. LAMA showed best performance among all planners in the sequential satisficing track of the International Planning Competition 2008. In this paper we present the system in detail and investigate which features of LAMA are crucial for its performance. We present individual results for some of the domains used at the competition, demonstrating good and bad cases for the techniques implemented in LAMA. Overall, we find that using landmarks improves performance, whereas the incorporation of action costs into the heuristic estimators proves not to be beneficial. We show that in some domains a search that ignores cost solves far more problems, raising the question of how to deal with action costs more effectively in the future. The iterated weighted A * search greatly improves results, and shows synergy effects with the use of landmarks.
Approximate Policy Iteration with a Policy Language Bias
- Journal of Artificial Intelligence Research
, 2003
"... We explore approximate policy iteration (API), replacing the usual costfunction learning step with a learning step in policy space. We give policy-language biases that enable solution of very large relational Markov decision processes (MDPs) that no previous technique can solve. ..."
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Cited by 140 (18 self)
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We explore approximate policy iteration (API), replacing the usual costfunction learning step with a learning step in policy space. We give policy-language biases that enable solution of very large relational Markov decision processes (MDPs) that no previous technique can solve.
LPG: A planner based on local search for planning graphs
- In Proc. of 6th Int. Conf. on AI Planning Systems (AIPS’02
"... We present LPG, a fast planner using local search for solving planning graphs. LPG can use various heuristics based on a parametrized objective function. These parameters weight different types of inconsistencies in the partial plan represented by the current search state, and are dynamically evalua ..."
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Cited by 129 (6 self)
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We present LPG, a fast planner using local search for solving planning graphs. LPG can use various heuristics based on a parametrized objective function. These parameters weight different types of inconsistencies in the partial plan represented by the current search state, and are dynamically evaluated during search using Lagrange multipliers. LPG’s basic heuristic was inspired by Walksat, which in Kautz and Selman’s Blackbox can be used to solve the SAT-encoding of a planning graph. An advantage of LPG is that its heuristics exploit the structure of the planning graph, while Blackbox relies on general heuristics for SAT-problems, and requires the translation of the planning graph into propositional clauses. Another major difference is that LPG can handle action costs to produce good quality plans. This is achieved by an “anytime ” process minimizing an objective function based on the number of inconsistencies in the partial plan and on its overall cost. The objective function can also take into account the number of parallel steps and the overall plan duration. Experimental results illustrate the efficiency of our approach showing, in particular, that for a set of well-known benchmark domains LPG is significantly faster than existing Graphplan-style planners.
A Planning Heuristic Based on Causal Graph Analysis
"... In recent years, heuristic search methods for classical planning have achieved remarkable results. Their most successful representative, the FF algorithm, performs well over a wide spectrum of planning domains and still sets the state of the art for STRIPS planning. However, there are some planning ..."
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Cited by 119 (18 self)
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In recent years, heuristic search methods for classical planning have achieved remarkable results. Their most successful representative, the FF algorithm, performs well over a wide spectrum of planning domains and still sets the state of the art for STRIPS planning. However, there are some planning domains in which algorithms like FF and HSP perform poorly because their relaxation method of ignoring the “delete lists” of STRIPS operators loses too much vital information. Planning domains which have many dead ends in the search space are especially problematic in this regard. In some domains, dead ends are readily found by the human observer yet remain undetected by all propositional planning systems we are aware of. We believe that this is partly because the STRIPS representation obscures the important causal structure of the domain, which is evident to humans. In this paper, we propose translating STRIPS problems to a planning formalism with multi-valued state variables in order to expose this underlying causal structure. Moreover, we show how this structure can be exploited by an algorithm for detecting dead ends in the search space and by a planning heuristic based on hierarchical problem decomposition. Our experiments show excellent overall performance on the benchmarks from the international planning competitions.
Landmarks, Critical Paths and Abstractions: What’s the Difference Anyway?
, 2009
"... Current heuristic estimators for classical domain-independent 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 112 (28 self)
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Current heuristic estimators for classical domain-independent 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.
TALplanner: A temporal logic based forward chaining planner
- ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE
, 2001
"... We present TALplanner, a forward-chaining planner based on the use of domaindependent
search control knowledge represented as formulas in the Temporal Action
Logic (TAL). TAL is a narrative based linear metric time logic used for reasoning
about action and change in incompletely speci#12;ed dynamic ..."
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Cited by 95 (17 self)
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We present TALplanner, a forward-chaining planner based on the use of domaindependent
search control knowledge represented as formulas in the Temporal Action
Logic (TAL). TAL is a narrative based linear metric time logic used for reasoning
about action and change in incompletely speci#12;ed dynamic environments. TAL
is used as the formal semantic basis for TALplanner, where a TAL goal narrative
with control formulas is input to TALplanner which then generates a TAL narrative
that entails the goal and control formulas. The sequential version of TALplanner is
presented. The expressivity of plan operators is then extended to deal with an interesting
class of resource types. An algorithm for generating concurrent plans, where
operators have varying durations and internal state, is also presented. All versions
of TALplanner have been implemented. The potential of these techniques is demonstrated
by applying TALplanner to a number of standard planning benchmarks in
the literature.
