[Om-announce] Call for Papers: OptLearnMAS workshop at AAMAS 2024

[OptLearnMAS]

Dear Colleague,

The OptLearnMAS workshop at AAMAS 2024 is accepting submissions!

The goal of the workshop is to provide researchers with a venue to discuss models or techniques for tackling a variety of multi-agent optimization problems. We seek contributions in the general area of multi-agent optimization, including distributed optimization, coalition formation, optimization under uncertainty, winner determination algorithms in auctions and procurements, and algorithms to compute Nash and other equilibria in games. Of particular emphasis are contributions at the intersection of optimization and learning. See below for a (non-exhaustive) list of topics.

This workshop invites works from different strands of the multi-agent systems community that pertain to the design of algorithms, models, and techniques to deal with multi-agent optimization and learning problems or problems that can be effectively solved by adopting a multi-agent framework.

Topics

The workshop organizers invite paper submissions on the following (and related) topics:

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Optimization for learning (strategic and non-strategic) agents

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Learning for multi-agent optimization problems

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Distributed constraint satisfaction and optimization

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Winner determination algorithms in auctions and procurements

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Coalition or group formation algorithms

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Algorithms to compute Nash and other equilibria in games

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Optimization under uncertainty

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Optimization with incomplete or dynamic input data

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Algorithms for real-time applications

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Cloud, distributed and grid computing

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Applications of learning and optimization in societally beneficial domains

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Multi-agent planning

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Multi-robot coordination

The workshop is of interest both to researchers investigating applications of multi-agent systems to optimization problems in large, complex domains, as well as to those examining optimization and learning problems that arise in systems comprised of many autonomous agents. In so doing, this workshop aims to provide a forum for researchers to discuss common issues that arise in solving optimization and learning problems in different areas, to introduce new application domains for multi-agent optimization techniques, and to elaborate common benchmarks to test solutions.

Finally, the workshop will welcome papers that describe the release of benchmarks and data sets that can be used by the community to solve fundamental problems of interest, including in machine learning and optimization for health systems and urban networks, to mention but a few examples.

Visit the website:

https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Foptlearnmas.github.io%2F&data=05%7C02%7C%7Cc20642e23031478cd02b08dc125ff9a8%7Ccc7df24760ce4a0f9d75704cf60efc64%7C0%7C0%7C638405451242457847%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C62000%7C%7C%7C&sdata=HI5KaYQQ%2BUvy6snAlDDiI61fNeQeG1mV4kS2eYzdOQU%3D&reserved=0

Important Dates

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Feb 5, 2024 (23:59 UTC-12) – Submission Deadline

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Mar 4, 2024 (23:59 UTC-12) – Acceptance notification

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May 6-7, 2024 – Workshop Date

Cheers,

Filippo Bistaffa, Hau Chan, Jiaoyang Li, and Xinrun Wang

OptLearnMAS-24 Co-Chairs
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