JAAMAS
@aamasjournal
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The editors-in-chief of the Springer journal, Autonomous Agents and Multi-Agent Systems. NEW: on Mastodon at @[email protected]
Joined June 2017
We are now also on Mastodon at @aamasjournal@masto.ai . We will (at least for now) be posting the same content on both platforms.
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Recent advances in leveraging human guidance for sequential decision-making tasks, by @RuohanZhang76, Faraz Torabi, Garrett Warnell & @PeterStone_TX (Oct 2021 issue) (2/2).
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How do humans transfer their knowledge and skills to artificial decision-making agents? What kind of knowledge and skills should humans provide and in what format? (1/2)
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Maximin fairness with mixed divisible and indivisible goods, by Xiaohui Bei, Shengxin Liu, Xinhang Lu & Hongao Wang (Oct 2021 issue) .
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Utility distribution matters: enabling fast belief propagation for multi-agent optimization with dense local utility function, by Yanchen Deng & Bo An (October 2021 issue)
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Testing stability properties in graphical hedonic games, by Hendrik Fichtenberger & Anja Rey (October 2021 issue)
link.springer.com
Autonomous Agents and Multi-Agent Systems - In hedonic games, players form coalitions based on individual preferences over the group of players they could belong to. Several concepts to describe...
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Concurrent local negotiations with a global utility function: a greedy approach, by @yasserfarouk (October 2021 issue): A new method for concurrent negotiation with theoretical guarantees on performance.#autoneg #automated_negotiation.
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. like an increasing number of agents or the exponential explosion of the number of joint actions. (3/4).
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In this work we investigate the effectiveness of factored methods in learning an accurate representation of the joint action-value function in cooperative multi-agent systems, and how these can address some of the traditional issues related to these settings, . (2/4)
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Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning, by Jacopo Castellini, @faoliehoek, @rahul__savani & @shimon8282 (October 2021 issue) (1/4)
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ANEGMA is a novel deep reinforcement learning-based negotiation model that allows an agent to learn how to negotiate during concurrent one-to-many bilateral negotiations in unknown and dynamic e-markets like E-bay. (2/2).
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ANEGMA: an automated negotiation model for e-markets, by @p_bagga_, @nicolapaoletti, Bedour Alrayes & @kstathis (October 2021 issue) (1/2).#DeepReinforcementLearning #AutomatedNegotiation #MultiAgentSystems #ReinforcementLearning #ComputerScience
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"Information Design in Affiliate Marketing", by Sharadhi Alape Suryanarayana, David Sarne & Sarit Kraus, in the October, 2021 issue of JAAMAS:
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Voting with random classifiers (VORACE) is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Paper by @Cristina__C, Michele Donini, @aloreggia, Maria Silvia Pini & @frossi_t (October 2021 issue):
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"The effect of strategic noise in linear regression", by @SafwanHossain14 and @nsrg_shah (Oct 2021) characterizes the impact on regression algorithms when strategic agents misreport training data to max some private objective #MachineLearning #gametheory
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. to see what other institutions and countries have agreements to cover open access fees see: (2/2).
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JAAMAS is included - so if you're based in Australia or New Zealand, your open access fees are covered (you need to be the corresponding author). (1/2).
If you’re affiliated with any of the 47 universities in Australia and New Zealand, or at one of the seven additional participating institutions in the region, you can publish your article open access with fees covered, in more than 2,000 Springer hybrid journals. Find out more.
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