The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks

  title={The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks},
  author={Silvia Richter and Matthias Westphal},
LAMA is a classical planning system based on heuristic forward search. [] Key Method 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…
Lama 2008 and 2011
Two versions of LAMA are described: the original LAMA as developed for the 2008 competition and a new re-implementation that uses the latest version of the Fast Downward Planning Framework.
Improving Plan Quality through Heuristics for Guiding and Pruning the Search: A Study Using LAMA
The empirical results show that the use of admissible heuristics in LAMA can be of great help to improve the planner performance.
An Admissible Heuristic for SAS+ Planning Obtained from the State Equation
A new admissible heuristic is obtained from the state equation associated to the Petri-net representation of the planning problem that can be computed in polynomial time and is competitive with the current state of the art for optimal planning, as empirically demonstrated over a large number of problems.
Better Time Constrained Search via Randomization and Postprocessing
By using the use of restarts, randomization, and not bounding as strictly as is done by previous approaches, the new anytime search framework of Diverse Any-Time Search is able to generate a more diverse set of "raw" input plans for the post-processor to work on.
Solving Planning Problems with LRTA*
The authors propose to replace the use of EHC and BFS with LRTA*, which is a search algorithm guided by heuristics and is as complete as BFS, and implemented some optimizations on LRTA, which shows significant improvements compared to the standard form of the FF planner.
Generating Plans Using LRTA
A new planning method based on LRTA is presented, which is an algorithm guided by heuristics like EHC and is complete as BFS and has some optimizations like pruning successors during expansion and a heap with maximum capacity to store the states during the search.
On the Use of Landmarks in LPG
This work proposes the use of Landmarks for the LPG planner, considering different design choices and analysing empirically its impact on the performance of the planner, finding results comparable with the state-of-the-art planner LAMA.
Width-based Algorithms for Classical Planning: New Results
Two simple extension to Iterated Width and Serialized IW are introduced that narrow the performance gap with state-of-the-art planners.
Parallel heuristic search in forward partial-order planning
Experimental results show that FLAP2 presents a very acceptable trade-off between time and quality and a high coverage on the current planning benchmarks, compared with four state-of-the-art planners.
Scaling-up Generalized Planning as Heuristic Search with Landmarks
A landmark counting heuristic for GP (that considers sub-goal in- formation that is not explicitly represented in the planning in-stances), and a novel heuristic search algorithm for GP and that progressively processes subsets of the planning instances of a GP problem.


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.
The Fast Downward Planning System
  • M. Helmert
  • Computer Science
    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.
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.
Planning as heuristic search
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.
A Lookahead Strategy for Heuristic Search Planning
This work presents a novel way for extracting information from the relaxed plan and for dealing with helpful actions, by considering the high quality of the relaxed plans in numerous domains, in a complete best-first search algorithm.
Sapa: A Scalable Multi-objective Heuristic Metric Temporal Planner
The technical details of extracting the heuristics are described and an empirical evaluation of the current implementation of Sapa is presented, one of the best domain independent planners for domains with metric and temporal constraints in the third International Planning Competition.
Anytime search in dynamic graphs
Temporal Planning using Subgoal Partitioning and Resolution in SGPlan
This paper presents a partition-and-resolve strategy that looks for locally optimal subplans in constraint-partitioned temporal planning subpro problems and that resolves those inconsistent global constraints across the subproblems.
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.