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Documents authored by Yeoh, William

Document
DOI: 10.4230/LIPIcs.CP.2024.24
Latency-Aware 2-Opt Monotonic Local Search for Distributed Constraint Optimization

Authors: Ben Rachmut, Roie Zivan, and William Yeoh

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
Researchers recently extended Distributed Constraint Optimization Problems (DCOPs) to Communication-Aware DCOPs so that they are applicable in scenarios in which messages can be arbitrarily delayed. Distributed asynchronous local search and inference algorithms designed for CA-DCOPs are less vulnerable to message latency than their counterparts for regular DCOPs. However, unlike local search algorithms for (regular) DCOPs that converge to k-opt solutions (with k > 1), that is, they converge to solutions that cannot be improved by a group of k agents), local search CA-DCOP algorithms are limited to 1-opt solutions only. In this paper, we introduce Latency-Aware Monotonic Distributed Local Search-2 (LAMDLS-2), where agents form pairs and coordinate bilateral assignment replacements. LAMDLS-2 is monotonic, converges to a 2-opt solution, and is also robust to message latency, making it suitable for CA-DCOPs. Our results indicate that LAMDLS-2 converges faster than MGM-2, a benchmark algorithm, to a similar 2-opt solution, in various message latency scenarios.
Cite as

Ben Rachmut, Roie Zivan, and William Yeoh. Latency-Aware 2-Opt Monotonic Local Search for Distributed Constraint Optimization. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 24:1-24:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{rachmut_et_al:LIPIcs.CP.2024.24,
  author =	{Rachmut, Ben and Zivan, Roie and Yeoh, William},
  title =	{{Latency-Aware 2-Opt Monotonic Local Search for Distributed Constraint Optimization}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{24:1--24:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.24},
  URN =		{urn:nbn:de:0030-drops-207096},
  doi =		{10.4230/LIPIcs.CP.2024.24},
  annote =	{Keywords: Distributed Constraint Optimization Problems, Distributed Local Search Algorithms, Latency Awareness, Multi-Agent Optimization}
}
Document
DOI: 10.4230/LIPIcs.CP.2024.33
Ex-Ante Constraint Elicitation in Incomplete DCOPs

Authors: Roie Zivan, Shiraz Regev, and William Yeoh

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
Distributed Constraint Optimization Problems (DCOPs) is a framework for representing and solving distributed combinatorial problems, where agents exchange messages to assign variables they own, such that the sum of constraint costs is minimized. When agents represent people (e.g., in meeting scheduling problems), the constraint information that the agents hold may be incomplete. For such scenarios, researchers proposed Incomplete DCOPs (I-DCOPs), which allow agents to elicit from their human users some of the missing information. Existing I-DCOP approaches evaluate solutions not only by their quality, but also the elicitation costs spent to find them (ex-post). Unfortunately, this may result in the agents spending a lot of effort (in terms of elicitation costs) to find high-quality solutions, and then ignoring them because previous lower-quality solutions were found with less effort. Therefore, we propose a different approach for solving I-DCOPs by evaluating solutions based on their quality and considering the elicitation cost beforehand (ex-ante). Agents are limited in the amount of information that they can elicit and, therefore, need to make smart decisions on choosing which missing information to elicit. We propose several heuristics for making these decisions. Our results indicate that some of the heuristics designed produce high-quality solutions, which significantly outperform the previously proposed ex-post heuristics.
Cite as

Roie Zivan, Shiraz Regev, and William Yeoh. Ex-Ante Constraint Elicitation in Incomplete DCOPs. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 33:1-33:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{zivan_et_al:LIPIcs.CP.2024.33,
  author =	{Zivan, Roie and Regev, Shiraz and Yeoh, William},
  title =	{{Ex-Ante Constraint Elicitation in Incomplete DCOPs}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{33:1--33:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.33},
  URN =		{urn:nbn:de:0030-drops-207182},
  doi =		{10.4230/LIPIcs.CP.2024.33},
  annote =	{Keywords: Distributed Constraint Optimization Problems, Preference Elicitation, Multi-Agent Optimization}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.60
The Effect of Asynchronous Execution and Message Latency on Max-Sum

Authors: Roie Zivan, Omer Perry, Ben Rachmut, and William Yeoh

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

Abstract
Max-sum is a version of belief propagation that was adapted for solving distributed constraint optimization problems (DCOPs). It has been studied theoretically and empirically, extended to versions that improve solution quality and converge rapidly, and is applicable to multiple distributed applications. The algorithm was presented both as a synchronous and an asynchronous algorithm, however, neither the differences in the performance of these two execution versions nor the implications of message latency on the two versions have been investigated to the best of our knowledge. We contribute to the body of knowledge on Max-sum by: (1) Establishing the theoretical differences between the two execution versions of the algorithm, focusing on the construction of beliefs; (2) Empirically evaluating the differences between the solutions generated by the two versions of the algorithm, with and without message latency; and (3) Establishing both theoretically and empirically the positive effect of damping on reducing the differences between the two versions. Our results indicate that in contrast to recent published results indicating the drastic effect that message latency has on distributed local search, damped Max-sum is robust to message latency.
Cite as

Roie Zivan, Omer Perry, Ben Rachmut, and William Yeoh. The Effect of Asynchronous Execution and Message Latency on Max-Sum. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 60:1-60:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{zivan_et_al:LIPIcs.CP.2021.60,
  author =	{Zivan, Roie and Perry, Omer and Rachmut, Ben and Yeoh, William},
  title =	{{The Effect of Asynchronous Execution and Message Latency on Max-Sum}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{60:1--60:18},
  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.2021.60},
  URN =		{urn:nbn:de:0030-drops-153518},
  doi =		{10.4230/LIPIcs.CP.2021.60},
  annote =	{Keywords: Distributed constraints, Distributed problem solving}
}
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