State-variable planning under structural restrictions: algorithms and complexity
Peter Jonsson,Christer Bäckström +1 more
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TLDR
This work identifies restrictions on the underlying state-transition graph which can tractably be tested and presents a planning algorithm which is correct and runs in polynomial time under these restrictions, and presents an exhaustive map of the complexity results for planning under all combinations of four previously studied syntactical restrictions and five new structural restrictions.About:
This article is published in Artificial Intelligence.The article was published on 1998-04-01 and is currently open access. It has received 116 citations till now. The article focuses on the topics: Graphplan & Time complexity.read more
Citations
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The fast downward planning system
TL;DR: Fast Downward as discussed by the authors 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.
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The LAMA planner: guiding cost-based anytime planning with landmarks
Silvia Richter,Matthias Westphal +1 more
TL;DR: 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.
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Concise finite-domain representations for PDDL planning tasks
TL;DR: An efficient method for translating planning tasks specified in the standard PDDL formalism into a concise grounded representation that uses finite-domain state variables instead of the straight-forward propositional encoding is introduced.
Proceedings Article
A planning heuristic based on causal graph analysis
TL;DR: This paper proposes translating STRIPS problems to a planning formalism with multi-valued state variables in order to expose this underlying causal structure of the domain, and shows 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.
Proceedings Article
Landmarks revisited
TL;DR: In this article, a novel approach for using landmarks in planning by deriving a pseudo-heuristic and combining it with other heuristics in a search framework was proposed. But the use of landmarks during search was not explored.
References
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Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
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A note on two problems in connexion with graphs
TL;DR: A tree is a graph with one and only one path between every two nodes, where at least one path exists between any two nodes and the length of each branch is given.
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Depth-First Search and Linear Graph Algorithms
TL;DR: The value of depth-first search or “backtracking” as a technique for solving problems is illustrated by two examples of an improved version of an algorithm for finding the strongly connected components of a directed graph.
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Strips: A new approach to the application of theorem proving to problem solving
Richard Fikes,Nils J. Nilsson +1 more
TL;DR: In this paper, the authors describe a problem solver called STRIPS that attempts to find a sequence of operators in a space of world models to transform a given initial world model in which a given goal formula can be proven to be true.