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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.
Landmarks Revisited
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.
A Stochastic Local Search Approach to Vertex Cover
TLDR
This work introduces a novel stochastic local search algorithm for the vertex cover problem and evaluates its performance on the commonly used DIMACS benchmarks for the related clique problem, finding that its approach is competitive with the current best stochastically local search algorithms for finding cliques.
Natural Actor-Critic for Road Traffic Optimisation
TLDR
A policy-gradient reinforcement learning approach is used to directly optimise the traffic signals, mapping currently deployed sensor observations to control signals and extending natural-actor critic approaches to work for distributed and online infinite-horizon problems.
Sound and Complete Landmarks for And/Or Graphs
TLDR
It is demonstrated that this approach finds strictly more causal landmarks than previous approaches and the relationship between increased computational effort and experimental performance is discussed, using these landmarks in a recently proposed admissible landmark-counting heuristic.
Lama 2008 and 2011
TLDR
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.
Preferred Operators and Deferred Evaluation in Satisficing Planning
TLDR
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.
The Joy of Forgetting: Faster Anytime Search via Restarting
TLDR
A new anytime approach that restarts the search from the initial state every time a new solution is found is presented, particularly useful for problems where the heuristic has systematic errors.
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 a
BJOLP: The Big Joint Optimal Landmarks Planner
BJOLP, the Big Joint Optimal Landmarks Planner, uses landmarks to derive an admissible heuristic, which is then used to guide a search for a cost-optimal plan. In this paper we review landmarks and
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