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A social ranking solution, or, briefly, a social ranking, over a set N is defined as a map assigning to each coalitional relation (i.e. a ranking over subsets of N) another ranking over the single elements in N. Differently, coalition formation situations, and, in particular, hedonic games, mainly focus on partitions of the set N into disjoint coalitions, which are in general referred to as coalition structures. A coalition structure may be stable according to various notions of stability and the objective is to understand under which conditions a coalition structure is stable. 
In this paper we merge the framework of coalition formation with the one of social rankings to keep into account the effect of hierarchies within coalitions on the stability of coalition structures. We consider alternative classes of coalition formation games where the preferences of the agents over coalitions are induced by a social ranking. More precisely, agents compare coalition structures keeping into account both the relative ranking of coalitions to which they belong (according to a coalitional relation) and their position in the social ranking within each coalition. Constructive characterizations of the set of core-stable coalition structures are provided for alternative classes of hedonic games, together with an impossibility theorem on the existence of core-stable coalitions structures for (nonhedonic) coalition formation situations.

AAMAS 2022

Coalition Formation Games and Social Ranking Solutions

coalition formation; hedonic games; social ranking; core stability

AAMAS is the largest and most influential conference in the area of agents and multiagent systems, bringing together researchers and practitioners in all areas of agent technology and providing and internationally renowned high-profile forum for publishing and finding out about the latest developments in the field. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).  

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AAMAS 2021

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AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).


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Trust repair in human-agent teams: the effectiveness ofexplanations and expressing regret


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Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions


Wind farms suffer from the so-called wake effects: when some of the turbines are located in the wakes of other turbines, their power output is substantially reduced. These losses can be partially mitigated via actively changing the yaw from the individually optimal direction. Many of the existing wake control techniques have two major limitations: they use simplified wake models to optimize the control strategy, and they assume that the atmospheric conditions remain stable. In this paper, we address these limitations by applying reinforcement learning (RL) to the active wake control problem. RL forgoes the wake model entirely and learns the optimal control strategy based on the observed atmospheric conditions and the reward signal, in this case the power output of the farm. It also accounts for random transitions in the observations, such as turbulent fluctuations in the wind. To evaluate the benefits of RL for active wake control, we implement a simulator based on the state-of-the-art FLORIS model in the OpenAI gym format, making it readily available to the RL community. Next, we propose three different state-action representations of the active wake control problem and investigate their effect on the performance of RL-based wake control. Finally, we compare RL to a state-of-the-art wake control strategy based on FLORIS and show that RL is less sensitive to changes in unobservable data.

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In this paper we consider a broader role of learning, impacting on the full spectrum of agent programming and engineering - from design to runtime. In particular, we introduce a perspective in which agent development is conceived as a kind developmental learning process of the agent itself. In that view, a software agent "is born" with some core domain-independent learning capabilities at the architectural level and is grown up in a proper learning environment to acquire the proper skills to be able then to achieve its designed objectives once it will be deployed, analogously to children in the human case.

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Coalition Formation Games and Social Ranking Solutions

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Coalition Formation Games and Social Ranking Solutions

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Next from AAMAS 2022

Empirical Estimates on Hand Manipulation are Recoverable: A Step Towards Individualized and Explainable Robotic Support in Everyday Activities

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Downloads