Alphazzle: Jigsaw Puzzle Solver with Deep Monte-Carlo Tree Search Article Swipe
Marie-Morgane Paumard
,
Hedi Tabia
,
David Picard
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.00384
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2302.00384
Solving jigsaw puzzles requires to grasp the visual features of a sequence of patches and to explore efficiently a solution space that grows exponentially with the sequence length. Therefore, visual deep reinforcement learning (DRL) should answer this problem more efficiently than optimization solvers coupled with neural networks. Based on this assumption, we introduce Alphazzle, a reassembly algorithm based on single-player Monte Carlo Tree Search (MCTS). A major difference with DRL algorithms lies in the unavailability of game reward for MCTS, and we show how to estimate it from the visual input with neural networks. This constraint is induced by the puzzle-solving task and dramatically adds to the task complexity (and interest!). We perform an in-deep ablation study that shows the importance of MCTS and the neural networks working together. We achieve excellent results and get exciting insights into the combination of DRL and visual feature learning.
Related Topics
Concepts
Monte Carlo tree search
Computer science
Jigsaw
Tree (set theory)
Artificial intelligence
Task (project management)
Artificial neural network
Reinforcement learning
Sequence (biology)
GRASP
Feature (linguistics)
Deep neural networks
Unavailability
Machine learning
Monte Carlo method
Mathematics
Economics
Philosophy
Programming language
Mathematics education
Statistics
Genetics
Management
Linguistics
Mathematical analysis
Biology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2302.00384
- https://arxiv.org/pdf/2302.00384
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319049496
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4319049496Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2302.00384Digital Object Identifier
- Title
-
Alphazzle: Jigsaw Puzzle Solver with Deep Monte-Carlo Tree SearchWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-01Full publication date if available
- Authors
-
Marie-Morgane Paumard, Hedi Tabia, David PicardList of authors in order
- Landing page
-
https://arxiv.org/abs/2302.00384Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2302.00384Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2302.00384Direct OA link when available
- Concepts
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Monte Carlo tree search, Computer science, Jigsaw, Tree (set theory), Artificial intelligence, Task (project management), Artificial neural network, Reinforcement learning, Sequence (biology), GRASP, Feature (linguistics), Deep neural networks, Unavailability, Machine learning, Monte Carlo method, Mathematics, Economics, Philosophy, Programming language, Mathematics education, Statistics, Genetics, Management, Linguistics, Mathematical analysis, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.this | 36, 49 |
| abstract_inverted_index.with | 24, 44, 68, 91 |
| abstract_inverted_index.(DRL) | 33 |
| abstract_inverted_index.Based | 47 |
| abstract_inverted_index.Carlo | 61 |
| abstract_inverted_index.MCTS, | 79 |
| abstract_inverted_index.Monte | 60 |
| abstract_inverted_index.based | 57 |
| abstract_inverted_index.grasp | 5 |
| abstract_inverted_index.grows | 22 |
| abstract_inverted_index.input | 90 |
| abstract_inverted_index.major | 66 |
| abstract_inverted_index.shows | 118 |
| abstract_inverted_index.space | 20 |
| abstract_inverted_index.study | 116 |
| abstract_inverted_index.Search | 63 |
| abstract_inverted_index.answer | 35 |
| abstract_inverted_index.jigsaw | 1 |
| abstract_inverted_index.neural | 45, 92, 125 |
| abstract_inverted_index.reward | 77 |
| abstract_inverted_index.should | 34 |
| abstract_inverted_index.visual | 7, 29, 89, 143 |
| abstract_inverted_index.(MCTS). | 64 |
| abstract_inverted_index.Solving | 0 |
| abstract_inverted_index.achieve | 130 |
| abstract_inverted_index.coupled | 43 |
| abstract_inverted_index.explore | 16 |
| abstract_inverted_index.feature | 144 |
| abstract_inverted_index.in-deep | 114 |
| abstract_inverted_index.induced | 97 |
| abstract_inverted_index.length. | 27 |
| abstract_inverted_index.patches | 13 |
| abstract_inverted_index.perform | 112 |
| abstract_inverted_index.problem | 37 |
| abstract_inverted_index.puzzles | 2 |
| abstract_inverted_index.results | 132 |
| abstract_inverted_index.solvers | 42 |
| abstract_inverted_index.working | 127 |
| abstract_inverted_index.ablation | 115 |
| abstract_inverted_index.estimate | 85 |
| abstract_inverted_index.exciting | 135 |
| abstract_inverted_index.features | 8 |
| abstract_inverted_index.insights | 136 |
| abstract_inverted_index.learning | 32 |
| abstract_inverted_index.networks | 126 |
| abstract_inverted_index.requires | 3 |
| abstract_inverted_index.sequence | 11, 26 |
| abstract_inverted_index.solution | 19 |
| abstract_inverted_index.algorithm | 56 |
| abstract_inverted_index.excellent | 131 |
| abstract_inverted_index.introduce | 52 |
| abstract_inverted_index.learning. | 145 |
| abstract_inverted_index.networks. | 46, 93 |
| abstract_inverted_index.together. | 128 |
| abstract_inverted_index.Alphazzle, | 53 |
| abstract_inverted_index.Therefore, | 28 |
| abstract_inverted_index.algorithms | 70 |
| abstract_inverted_index.complexity | 108 |
| abstract_inverted_index.constraint | 95 |
| abstract_inverted_index.difference | 67 |
| abstract_inverted_index.importance | 120 |
| abstract_inverted_index.reassembly | 55 |
| abstract_inverted_index.assumption, | 50 |
| abstract_inverted_index.combination | 139 |
| abstract_inverted_index.efficiently | 17, 39 |
| abstract_inverted_index.interest!). | 110 |
| abstract_inverted_index.dramatically | 103 |
| abstract_inverted_index.optimization | 41 |
| abstract_inverted_index.exponentially | 23 |
| abstract_inverted_index.reinforcement | 31 |
| abstract_inverted_index.single-player | 59 |
| abstract_inverted_index.puzzle-solving | 100 |
| abstract_inverted_index.unavailability | 74 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |