Potential-based bounded-cost search and anytime non-parametric A*

DOI10.1016/j.artint.2014.05.002zbMath1405.68330OpenAlexW2126113409WikidataQ57365675 ScholiaQ57365675MaRDI QIDQ2014923

Rajat Shah, Ariel Felner, Roni Stern, Rami Puzis, Ken Goldberg, Jur P. van den Berg

Publication date: 16 June 2014

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2014.05.002

zbMATH Keywords

roboticsanytime algorithmsheuristic search

Mathematics Subject Classification ID

Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Artificial intelligence for robotics (68T40)

Related Items

Job sequencing with one common and multiple secondary resources: an A*/beam search based anytime algorithm ⋮ Probably bounded suboptimal heuristic search ⋮ Truncated incremental search

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A note on two problems in connexion with graphs
• Predicting the size of IDA\(^*\)'s search tree
• Linear-space best-first search
• Incremental deployment of network monitors based on Group Betweenness Centrality
• Depth-first iterative-deepening: An optimal admissible tree search
• Reducing reexpansions in iterative-deepening search by controlling cutoff bounds
• Enhanced \(A^{*}\) algorithms for multiple alignments: optimal alignments for several sequences and k-opt approximate alignments for large cases
• Solution counting algorithms for constraint-centered search heuristics
• Duality in permutation state spaces and the dual search algorithm
• Maximizing over multiple pattern databases speeds up heuristic search
• Heuristic search viewed as path finding in a graph
• Emergence of Scaling in Random Networks
• Studies in Semi-Admissible Heuristics
• The centrality of groups and classes
• Performance of linear-space search algorithms
• Time complexity of iterative-deepening-\(A^{*}\)