Corpus ID: 1568776

Lama 2008 and 2011

@inproceedings{Richter2011Lama2A,
  title={Lama 2008 and 2011},
  author={Silvia Richter and M. Westphal and M. Helmert},
  year={2011}
}
LAMA is a propositional planning system based on heuristic search with landmarks. This paper describes two versions of LAMA that were entered into the 2011 International Planning Competition: the original LAMA as developed for the 2008 competition and a new re-implementation of LAMA that uses the latest version of the Fast Downward Planning Framework. Landmarks are propositions that must be true in every solution of a planning task. LAMA uses a heuristic derived from landmarks in conjunction… Expand
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References

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The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks
TLDR
It is found that using landmarks improves performance, whereas the incorporation of action costs into the heuristic estimators proves not to be beneficial, and 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. Expand
The LAMA Planner Using Landmark Counting in Heuristic Search
LAMA is a propositional planning system based on heuristic search. Its core feature is the use of a pseudo-heuristic derived from landmarks, propositions that must be true in every solution of aExpand
The Fast Downward Planning System
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A full account of Fast Downward's approach to solving multivalued planning tasks is given and a new non-heuristic search algorithm called focused iterative-broadening search, which utilizes the information encoded in causal graphs in a novel way is presented. Expand
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TLDR
A novel search strategy is introduced that combines hill-climbing with systematic search, and it is shown how other powerful heuristic information can be extracted and used to prune the search space. Expand
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TLDR
A family of heuristic search planners are studied based on a simple and general heuristic that assumes that action preconditions are independent, which is used in the context of best-first and hill-climbing search algorithms, and tested over a large collection of domains. Expand
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TLDR
This work proposes a novel approach for using landmarks in planning by deriving a pseudo-heuristic and combining it with other heuristics in a search framework and shows how additional landmarks and orderings can be found using the information present in multi-valued state variable representations of planning tasks. Expand
Heuristics for Planning with Action Costs Revisited
TLDR
A simple variation of the additive heuristic used in the HSP planner is introduced that combines the benefits of the original additiveHeuristic, namely its mathematical formulation and its ability to handle non-uniform action costs, with the benefit of the relaxed planning graph heuristic, and is shown to compare well with cost-sensitive planners. Expand
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This work extends Koehler and Hoffmann's definition of reasonable orders between top level goals to the more general case of landmarks and shows how landmarks can be found, how their reasonable orders can be approximated, and how this information can be used to decompose a given planning task into several smaller sub-tasks. Expand
On the extraction, ordering, and usage of landmarks in planning
TLDR
This work defines ordering constraints not only over the top level goals, but also over the sub-goals that will arise during planning, and demonstrates that the approach can yield significant performance improvements in both heuristic forward search and Graphplan-style planning. Expand
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The use of preferred operators and deferred evaluation in a variety of settings within best-first search are examined, finding that they are consistent with and help explain the good performance of the winners of the satisficing tracks at IPC 2004 and 2008. Expand
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