Relaxed Core Stability in Fractional Hedonic Games

The core is a well-known and fundamental notion of stability in games intended to model coalition formation such as hedonic games. The fact that the number of deviating agents (that have to coordinate themselves) can be arbitrarily high, and the fact that agents may benefit only by a tiny amount from their deviation (while they could incur in a cost for deviating), suggest that the core is not able to suitably model many practical scenarios in large and highly distributed multi-agent systems. For this reason, we consider relaxed core stable outcomes where the notion of permissible deviations is modified along two orthogonal directions: the former takes into account the size of the deviating coalition, and the latter the amount of utility gain for each member of the deviating coalition. These changes result in two different notions of stability, namely, the q-size core and k-improvement core. We investigate these concepts of stability in fractional hedonic games, that is a well-known subclass of hedonic games for which core stable outcomes are not guaranteed to exist and it is computationally hard to decide nonemptiness of the core. Interestingly, the considered relaxed notions of core also possess the appealing property of recovering, in some notable cases, the convergence, the existence and the possibility of computing stable solutions in polynomial time.

Download Full-text

Loyalty in Cardinal Hedonic Games

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/10 ◽

2021 ◽

Author(s):

Martin Bullinger ◽

Stefan Kober

Keyword(s):

Coalition Formation ◽

Pareto Optimal ◽

Common Theme ◽

Multi Agent Systems ◽

Agent Systems ◽

The Core ◽

Coalition Structures ◽

Group Stability ◽

Multi Agent ◽

Hedonic Games

A common theme of decision making in multi-agent systems is to assign utilities to alternatives, which individuals seek to maximize. This rationale is questionable in coalition formation where agents are affected by other members of their coalition. Based on the assumption that agents are benevolent towards other agents they like to form coalitions with, we propose loyalty in hedonic games, a binary relation dependent on agents' utilities. Given a hedonic game, we define a loyal variant where agents' utilities are defined by taking the minimum of their utility and the utilities of agents towards which they are loyal. This process can be iterated to obtain various degrees of loyalty, terminating in a locally egalitarian variant of the original game. We investigate axioms of group stability and efficiency for different degrees of loyalty. Specifically, we consider the problem of finding coalition structures in the core and of computing best coalitions, obtaining both positive and intractability results. In particular, the limit game possesses Pareto optimal coalition structures in the core.

Download Full-text

A multi-responsibility–oriented coalition formation framework for dynamic task allocation in mobile–distributed multi-agent systems

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418813037 ◽

2018 ◽

Vol 15 (6) ◽

pp. 172988141881303 ◽

Cited By ~ 1

Author(s):

Bing Xie ◽

Xueqiang Gu ◽

Jing Chen ◽

LinCheng Shen

Keyword(s):

Coalition Formation ◽

Task Allocation ◽

Game Model ◽

Multi Agent Systems ◽

Coalition Game ◽

Task Execution ◽

Agent Systems ◽

Dynamic Task ◽

Dynamic Task Allocation ◽

Multi Agent

In this article, we study a problem of dynamic task allocation with multiple agent responsibilities in distributed multi-agent systems. Agents in the research have two responsibilities, communication and task execution. Movements in agent task execution bring changes to the system network structure, which will affect the communication. Thus, agents need to be autonomous on communication network reconstruction for good performance on task execution. First, we analyze the relationships between the two responsibilities of agents. Then, we design a multi-responsibility–oriented coalition formation framework for dynamic task allocation with two parts, namely, task execution and self-adaptation communication. For the former part, we integrate our formerly proposed algorithm in the framework for task execution coalition formation. For the latter part, we develop a constrained Bayesian overlapping coalition game model to formulate the communication network. A task-allocation efficiency–oriented communication coalition utility function is defined to optimize a coalition structure for the constrained Bayesian overlapping coalition game model. Considering the geographical location dependence between the two responsibilities, we define constrained agent strategies to map agent strategies to potential location choices. Based on the abovementioned design, we propose a distributed location pruning self-adaptive algorithm for the constrained Bayesian overlapping coalition formation. Finally, we test the performance of our framework, multi-responsibility–oriented coalition formation framework, with simulation experiments. Experimental results demonstrate that the multi-responsibility oriented coalition formation framework performs better than the other two distributed algorithms on task completion rate (by over 9.4% and over 65% on average, respectively).

Download Full-text

Trust in multi-agent systems

The Knowledge Engineering Review ◽

10.1017/s0269888904000116 ◽

2004 ◽

Vol 19 (1) ◽

pp. 1-25 ◽

Cited By ~ 343

Author(s):

SARVAPALI D. RAMCHURN ◽

DONG HUYNH ◽

NICHOLAS R. JENNINGS

Keyword(s):

Large Scale ◽

State Of The Art ◽

Comprehensive Treatment ◽

Multi Agent Systems ◽

Agent Based ◽

Agent Systems ◽

The Core ◽

Open Distributed Systems ◽

Multi Agent

Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.

Download Full-text

Hierarchical Coalition Formation in Multi-agent Systems

Distributed Computing and Artificial Intelligence, Special Sessions, 17th International Conference - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-030-53829-3_23 ◽

2020 ◽

pp. 210-214

Author(s):

Tabajara Krausburg

Keyword(s):

Coalition Formation ◽

Multi Agent Systems ◽

Agent Systems ◽

Multi Agent

Download Full-text

Study of Ant Colony Optimization Technique for Coalition Formation in Multi Agent Systems

2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET) ◽

10.1109/iccsdet.2018.8821175 ◽

2018 ◽

Author(s):

Neha Bhateja ◽

Nishu Sethi ◽

Deepak Kumar

Keyword(s):

Ant Colony Optimization ◽

Coalition Formation ◽

Optimization Technique ◽

Ant Colony ◽

Multi Agent Systems ◽

Agent Systems ◽

Multi Agent

Download Full-text

Contextual and Possibilistic Reasoning for Coalition Formation

AI ◽

10.3390/ai1030026 ◽

2020 ◽

Vol 1 (3) ◽

pp. 389-417

Author(s):

Antonis Bikakis ◽

Patrice Caire

Keyword(s):

Coalition Formation ◽

Multi Agent Systems ◽

Agent Systems ◽

Contextual Reasoning ◽

Multi Agent

In multi-agent systems, agents often need to cooperate and form coalitions to fulfil their goals, for example by carrying out certain actions together or by sharing their resources. In such situations, some questions that may arise are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents’ abilities to carry out certain tasks? In this article, we address the question of how to identify and evaluate the potential agent coalitions, while taking into consideration the uncertainty around the agents’ actions. Our methodology is the following: We model multi-agent systems as Multi-Context Systems, by representing agents as contexts and the dependencies among agents as bridge rules. Using methods and tools for contextual reasoning, we compute all possible coalitions with which the agents can fulfil their goals. Finally, we evaluate the coalitions using appropriate metrics, each corresponding to a different requirement. To demonstrate our approach, we use an example from robotics.

Download Full-text