Corpus ID: 1568776

Lama 2008 and 2011

  title={Lama 2008 and 2011},
  author={Silvia Richter and M. Westphal and M. Helmert},
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
Planning with Multistep Forward Search with Forced Goal‐Ordering Constraints
An approach via an effective search heuristic to constrain a planner to satisfy the FGOs is put forward, which makes use of an atomic goal‐achievement graph in a look‐ahead search under the F GO constraints and proves several formal properties for search that are related to FGO detection. Expand
Dynamic heuristic planner selection
  • Brian Cook, M. Huber
  • Computer Science
  • 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
  • 2016
A novel approach for planning that monitors the search dynamics of a heuristic planner over time in order to recognize whether the planner is making progress toward a solution and dynamically selects from a set of heuristic planners during the planning process so that planners that appear to be making progress are allocated more processor time. Expand
Scheduling for multiple type objects using POPStar planner
Results show that the POPStar algorithm can create and adapt schedules for robot cells with changing product types in low volume production. Expand
Planning Via Random Walk-Driven Local Search
Random Walk-Driven Local Search (RW-LS) is a strong new addition to this family of planning algorithms that uses a greedy best-first search driven by a combination of random walks and direct node evaluation. Expand
An Automatic Algorithm Selection Approach for Planning
Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting differentExpand
An Empirical Comparison of PDDL-based and ASP-based Task Planners
Comparing the performance of state-of-the-art planners that use either the Planning Domain Description Language (PDDL), or Answer Set Programming (ASP) as the underlying action language finds that PDDL- based planners perform better on problems with longer solutions, and ASP-based planners are better on tasks with a large number of objects. Expand
Learning and Tuning Meta-heuristics in Plan Space Planning
An online error minimization approach in POCL framework is discussed to minimize the step-error associated with the offline learned models thus enhancing their informativeness and scale up the performance of the planner over standard benchmarks, specially for larger problems. Expand
Inner Entanglements : Narrowing the Search in Classical Planning by Problem Reformulation
In the field of Automated Planning, a central research focus is on domain-independent planning engines which accept planning tasks (domain models and problem descriptions) in a description languageExpand
Task Planning in Robotics: an Empirical Comparison of PDDL-based and ASP-based Systems.
P empirically compare the performance of state-of-the-art planners that use either the Planning Domain Description Language (PDDL), or Answer Set Programming (ASP) as the underlying action language and finds that PDDL-based planners perform better on problems with longer solutions, and ASP- based planners are better on tasks with a large number of objects. Expand
Generalized Planning with Positive and Negative Examples
Experiments show that incorporating negative examples can accelerate plan synthesis in several domains and leverage quantitative metrics to evaluate the generalization capacity of the synthesized plans. Expand


The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks
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
  • M. Helmert
  • Computer Science, Mathematics
  • J. Artif. Intell. Res.
  • 2006
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
The FF Planning System: Fast Plan Generation Through Heuristic Search
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
Planning as heuristic search
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
Landmarks Revisited
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
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
Ordered Landmarks in Planning
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
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
Preferred Operators and Deferred Evaluation in Satisficing Planning
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