A Learning-Based Framework for Memory-Bounded Heuristic Search: First Results Article Swipe
Carlos Hernández
,
Jorge A. Baier
,
William Yeoh
,
Vadim Bulitko
,
Sven Koenig
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1609/socs.v10i1.18479
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1609/socs.v10i1.18479
Many existing boundedly-suboptimal heuristic search algorithms are variants of best-first search. Due to memory limitations, these algorithms are unable to solve problems with extremely large search spaces. In this paper, we present a framework that allows best-first search algorithms to solve problems with such large search spaces given a (reasonable) memory bound while also preserving optimality guarantees in tree-structured search spaces. In our framework, a given algorithm is run several times. In each search episode, the algorithm expands up to a user-defined number of states. After each episode, unless the goal has been found, the heuristic values of the generated states are updated using a linear-time algorithm that preserves consistency in tree-structured search spaces. In subsequent search episodes, only the heuristic values of the states generated in the previous episode need to be kept in memory. We present experimental results where we plug A*, GBFS, and wA* into our framework to solve traveling salesman problems and compare them against benchmark linear-memory algorithms like DFBnB and wDFBnB.
Related Topics
Concepts
Beam search
Best-first search
Incremental heuristic search
Benchmark (surveying)
Iterative deepening depth-first search
Heuristic
Null-move heuristic
Computer science
Depth-first search
Search algorithm
Mathematics
Algorithm
Heuristics
Mathematical optimization
Geography
Geodesy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/socs.v10i1.18479
- https://ojs.aaai.org/index.php/SOCS/article/download/18479/18270
- OA Status
- diamond
- Cited By
- 1
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2965614599
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2965614599Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1609/socs.v10i1.18479Digital Object Identifier
- Title
-
A Learning-Based Framework for Memory-Bounded Heuristic Search: First ResultsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-01Full publication date if available
- Authors
-
Carlos Hernández, Jorge A. Baier, William Yeoh, Vadim Bulitko, Sven KoenigList of authors in order
- Landing page
-
https://doi.org/10.1609/socs.v10i1.18479Publisher landing page
- PDF URL
-
https://ojs.aaai.org/index.php/SOCS/article/download/18479/18270Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ojs.aaai.org/index.php/SOCS/article/download/18479/18270Direct OA link when available
- Concepts
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Beam search, Best-first search, Incremental heuristic search, Benchmark (surveying), Iterative deepening depth-first search, Heuristic, Null-move heuristic, Computer science, Depth-first search, Search algorithm, Mathematics, Algorithm, Heuristics, Mathematical optimization, Geography, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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16Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.landing_page_url | https://doi.org/10.1609/socs.v10i1.18479 |
| publication_date | 2021-09-01 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W1570293800, https://openalex.org/W2293009092, https://openalex.org/W6655670082, https://openalex.org/W6652928594, https://openalex.org/W6653038996, https://openalex.org/W6660982047, https://openalex.org/W6659469833, https://openalex.org/W187834424, https://openalex.org/W6601538878, https://openalex.org/W2010549090, https://openalex.org/W2042872950, https://openalex.org/W2035601288, https://openalex.org/W4248795023, https://openalex.org/W38076005, https://openalex.org/W2009833344, https://openalex.org/W2021061679 |
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