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Documents authored by Chen, Di


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Chen, Di

Document
CLR-DRNets: Curriculum Learning with Restarts to Solve Visual Combinatorial Games

Authors: Yiwei Bai, Di Chen, and Carla P. Gomes

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)


Abstract
We introduce a curriculum learning framework for challenging tasks that require a combination of pattern recognition and combinatorial reasoning, such as single-player visual combinatorial games. Our work harnesses Deep Reasoning Nets (DRNets) [Chen et al., 2020], a framework that combines deep learning with constraint reasoning for unsupervised pattern demixing. We propose CLR-DRNets (pronounced Clear-DRNets), a curriculum-learning-with-restarts framework to boost the performance of DRNets. CLR-DRNets incrementally increase the difficulty of the training instances and use restarts, a new model selection method that selects multiple models from the same training trajectory to learn a set of diverse heuristics and apply them at inference time. An enhanced reasoning module is also proposed for CLR-DRNets to improve the ability of reasoning and generalize to unseen instances. We consider Visual Sudoku, i.e., Sudoku with hand-written digits or letters, and Visual Mixed Sudoku, a substantially more challenging task that requires the demixing and completion of two overlapping Visual Sudokus. We propose an enhanced reasoning module for the DRNets framework for encoding these visual games We show how CLR-DRNets considerably outperform DRNets and other approaches on these visual combinatorial games.

Cite as

Yiwei Bai, Di Chen, and Carla P. Gomes. CLR-DRNets: Curriculum Learning with Restarts to Solve Visual Combinatorial Games. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 17:1-17:14, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)


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@InProceedings{bai_et_al:LIPIcs.CP.2021.17,
  author =	{Bai, Yiwei and Chen, Di and Gomes, Carla P.},
  title =	{{CLR-DRNets: Curriculum Learning with Restarts to Solve Visual Combinatorial Games}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{17:1--17:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.17},
  URN =		{urn:nbn:de:0030-drops-153086},
  doi =		{10.4230/LIPIcs.CP.2021.17},
  annote =	{Keywords: Unsupervised Learning, Combinatorial Optimization}
}
Document
Sink Evacuation on Trees with Dynamic Confluent Flows

Authors: Di Chen and Mordecai Golin

Published in: LIPIcs, Volume 64, 27th International Symposium on Algorithms and Computation (ISAAC 2016)


Abstract
Let G = (V, E) be a graph modelling a building or road network in which edges have-both travel times (lengths) and capacities associated with them. An edge’s capacity is the number of people that can enter that edge in a unit of time. In emergencies, people evacuate towards the exits. If too many people try to evacuate through the same edge, congestion builds up and slows down the evacuation. Graphs with both lengths and capacities are known as Dynamic Flow networks. An evacuation plan for G consists of a choice of exit locations and a partition of the people at the vertices into groups, with each group evacuating to the same exit. The evacuation time of a plan is the time it takes until the last person evacuates. The k-sink evacuation problem is to provide an evacuation plan with k exit locations that minimizes the evacuation time. It is known that this problem is NP-Hard for general graphs but no polynomial time algorithm was previously known even for the case of G a tree. This paper presents an O(nk^2 log^5 n) algorithm for the k-sink evacuation problem on trees, which can also be applied to a more general class of problems.

Cite as

Di Chen and Mordecai Golin. Sink Evacuation on Trees with Dynamic Confluent Flows. In 27th International Symposium on Algorithms and Computation (ISAAC 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 64, pp. 25:1-25:13, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2016)


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@InProceedings{chen_et_al:LIPIcs.ISAAC.2016.25,
  author =	{Chen, Di and Golin, Mordecai},
  title =	{{Sink Evacuation on Trees with Dynamic Confluent Flows}},
  booktitle =	{27th International Symposium on Algorithms and Computation (ISAAC 2016)},
  pages =	{25:1--25:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-026-2},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{64},
  editor =	{Hong, Seok-Hee},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2016.25},
  URN =		{urn:nbn:de:0030-drops-67951},
  doi =		{10.4230/LIPIcs.ISAAC.2016.25},
  annote =	{Keywords: Sink Evacuation, Dynamic Flow, Facility Location, Parametric Search}
}

Chen, Dingding

Document
Completeness Matters: Towards Efficient Caching in Tree-Based Synchronous Backtracking Search for DCOPs

Authors: Jie Wang, Dingding Chen, Ziyu Chen, Xiangshuang Liu, and Junsong Gao

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)


Abstract
Tree-based backtracking search is an important technique to solve Distributed Constraint optimization Problems (DCOPs), where agents cooperatively exhaust the search space by branching on each variable to divide subproblems and reporting the results to their parent after solving each subproblem. Therefore, effectively reusing the historical search results can avoid unnecessary resolutions and substantially reduce the overall overhead. However, the existing caching schemes for asynchronous algorithms cannot be applied directly to synchronous ones, in the sense that child agent reports the lower and upper bound rather than the precise cost of exploration. In addition, the existing caching scheme for synchronous algorithms has the shortcomings of incompleteness and low cache utilization. Therefore, we propose a new caching scheme for tree-based synchronous backtracking search, named Retention Scheme (RS). It utilizes the upper bounds of subproblems which avoid the reuse of suboptimal solutions to ensure the completeness, and deploys a fine-grained cache information unit targeted at each child agent to improve the cache-hit rate. Furthermore, we introduce two new cache replacement schemes to further improve performance when the memory is limited. Finally, we theoretically prove the completeness of our method and empirically show its superiority.

Cite as

Jie Wang, Dingding Chen, Ziyu Chen, Xiangshuang Liu, and Junsong Gao. Completeness Matters: Towards Efficient Caching in Tree-Based Synchronous Backtracking Search for DCOPs. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 39:1-39:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)


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@InProceedings{wang_et_al:LIPIcs.CP.2022.39,
  author =	{Wang, Jie and Chen, Dingding and Chen, Ziyu and Liu, Xiangshuang and Gao, Junsong},
  title =	{{Completeness Matters: Towards Efficient Caching in Tree-Based Synchronous Backtracking Search for DCOPs}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{39:1--39:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.39},
  URN =		{urn:nbn:de:0030-drops-166685},
  doi =		{10.4230/LIPIcs.CP.2022.39},
  annote =	{Keywords: DCOP, Cache, Any-space Algorithms, Complete Search Algorithms}
}
Document
A Bound-Independent Pruning Technique to Speeding up Tree-Based Complete Search Algorithms for Distributed Constraint Optimization Problems

Authors: Xiangshuang Liu, Ziyu Chen, Dingding Chen, and Junsong Gao

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)


Abstract
Complete search algorithms are important methods for solving Distributed Constraint Optimization Problems (DCOPs), which generally utilize bounds to prune the search space. However, obtaining high-quality lower bounds is quite expensive since it requires each agent to collect more information aside from its local knowledge, which would cause tremendous traffic overheads. Instead of bothering for bounds, we propose a Bound-Independent Pruning (BIP) technique for existing tree-based complete search algorithms, which can independently reduce the search space only by exploiting local knowledge. Specifically, BIP enables each agent to determine a subspace containing the optimal solution only from its local constraints along with running contexts, which can be further exploited by any search strategies. Furthermore, we present an acceptability testing mechanism to tailor existing tree-based complete search algorithms to search the remaining space returned by BIP when they hold inconsistent contexts. Finally, we prove the correctness of our technique and the experimental results show that BIP can significantly speed up state-of-the-art tree-based complete search algorithms on various standard benchmarks.

Cite as

Xiangshuang Liu, Ziyu Chen, Dingding Chen, and Junsong Gao. A Bound-Independent Pruning Technique to Speeding up Tree-Based Complete Search Algorithms for Distributed Constraint Optimization Problems. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 41:1-41:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)


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@InProceedings{liu_et_al:LIPIcs.CP.2021.41,
  author =	{Liu, Xiangshuang and Chen, Ziyu and Chen, Dingding and Gao, Junsong},
  title =	{{A Bound-Independent Pruning Technique to Speeding up Tree-Based Complete Search Algorithms for Distributed Constraint Optimization Problems}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{41:1--41:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.41},
  URN =		{urn:nbn:de:0030-drops-153324},
  doi =		{10.4230/LIPIcs.CP.2021.41},
  annote =	{Keywords: DCOP, complete algorithms, search}
}
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