A Neural Network for Decision Making in Real-Time Heuristic Search Article Swipe
Franco Muñoz
,
M. Natalia Castillo Fadic
,
Carlos Hernández
,
Jorge A. Baier
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1609/socs.v9i1.18468
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1609/socs.v9i1.18468
Most real-time heuristic search algorithms solve search problems by executing a series of episodes. During each episode the algorithm decides an action for execution. Such a decision is usually made using information gathered by running a bounded, heuristic-search algorithm. In this paper we report on a real-time search algorithm that does not use a search algorithm to choose the next action to be applied. Rather, it uses a neural network whose input is local information about the search graph, comparable to the information that would be used by a bounded search algorithm. We describe a supervised learning approach to training such a network. Our three types of maps from the Moving AI benchmarks, shows that our algorithm is, in some cases, substantially superior to algorithms that have access to the same information about the graph. One of our most important conclusions is that our extended set of features important: indeed, using features beyond the heuristic seems key to achieving good performance.
Related Topics
Concepts
Incremental heuristic search
Beam search
Best-first search
Computer science
Iterative deepening depth-first search
Search algorithm
Artificial intelligence
Heuristic
Set (abstract data type)
Graph
Artificial neural network
Machine learning
Bidirectional search
Key (lock)
Bounded function
Action (physics)
Algorithm
Theoretical computer science
Mathematics
Programming language
Quantum mechanics
Computer security
Mathematical analysis
Physics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/socs.v9i1.18468
- https://ojs.aaai.org/index.php/SOCS/article/download/18468/18259
- OA Status
- diamond
- Cited By
- 5
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2886896059
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2886896059Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1609/socs.v9i1.18468Digital Object Identifier
- Title
-
A Neural Network for Decision Making in Real-Time Heuristic SearchWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-01Full publication date if available
- Authors
-
Franco Muñoz, M. Natalia Castillo Fadic, Carlos Hernández, Jorge A. BaierList of authors in order
- Landing page
-
https://doi.org/10.1609/socs.v9i1.18468Publisher landing page
- PDF URL
-
https://ojs.aaai.org/index.php/SOCS/article/download/18468/18259Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://ojs.aaai.org/index.php/SOCS/article/download/18468/18259Direct OA link when available
- Concepts
-
Incremental heuristic search, Beam search, Best-first search, Computer science, Iterative deepening depth-first search, Search algorithm, Artificial intelligence, Heuristic, Set (abstract data type), Graph, Artificial neural network, Machine learning, Bidirectional search, Key (lock), Bounded function, Action (physics), Algorithm, Theoretical computer science, Mathematics, Programming language, Quantum mechanics, Computer security, Mathematical analysis, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2022: 1, 2021: 2, 2020: 1Per-year citation counts (last 5 years)
- References (count)
-
16Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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