Coalition Formation In Multi-agent Systems Research Articles

Reaching Individually Stable Coalition Structures

The formal study of coalition formation in multi-agent systems is typically realized in the framework of hedonic games, which originate from economic theory. The main focus of this branch of research has been on the existence and the computational complexity of deciding the existence of coalition structures that satisfy various stability criteria. The actual process of forming coalitions based on individual behavior has received little attention. In this article, we study the convergence of simple dynamics leading to stable partitions in a variety of established classes of hedonic games, including anonymous, dichotomous, fractional, and hedonic diversity games. The dynamics we consider is based on individual stability: an agent will join another coalition if she is better off and no member of the welcoming coalition is worse off. Our results are threefold. First, we identify conditions for the (fast) convergence of our dynamics. To this end, we develop new techniques based on the simultaneous usage of multiple intertwined potential functions and establish a reduction uncovering a close relationship between anonymous hedonic games and hedonic diversity games. Second, we provide elaborate counterexamples determining tight boundaries for the existence of individually stable partitions. Third, we study the computational complexity of problems related to the coalition formation dynamics. In particular, we settle open problems suggested by Bogomolnaia and Jackson, Brandl et al., and Boehmer and Elkind.

Reaching Individually Stable Coalition Structures in Hedonic Games

The formal study of coalition formation in multiagent systems is typically realized using so-called hedonic games, which originate from economic theory. The main focus of this branch of research has been on the existence and the computational complexity of deciding the existence of coalition structures that satisfy various stability criteria. The actual process of forming coalitions based on individual behavior has received little attention. In this paper, we study the convergence of simple dynamics leading to stable partitions in a variety of classes of hedonic games, including anonymous, dichotomous, fractional, and hedonic diversity games. The dynamics we consider is based on individual stability: an agent will join another coalition if she is better off and no member of the welcoming coalition is worse off. We identify conditions for convergence, provide elaborate counterexamples of existence of individually stable partitions, and study the computational complexity of problems related to the coalition formation dynamics. In particular, we settle open problems suggested by Bogomolnaia and Jackson (2002), Brandl, Brandt, and Strobel (2015), and Boehmer and Elkind (2020).

Coalition formation algorithm based on organization and Markov decision process for multi-player pursuit evasion

Coalition formation in multi-agent systems seeks to create efficient agents' alliances in order to perform complex tasks. The main objective of this paper is to measure the impact of the pursuit groups' formation on the capture of different evaders.

Coalition Formation in Multi-agent Systems Based on Improved Particle Swarm Optimization Algorithm

How to generate the task-oriented optimal agent coalition is a key issue of multi-agent system, which is a typical optimization problem. In this paper, an improved particle swarm optimization (IPSO) is proposed to solve this problem. In order to overcome the premature and local optimization problem in traditional particle swarm optimization (PSO), we proposed a variation of inertia weight PSO algorithm by analyzing the feasibility of particle optimization process in PSO. Compared with several well-known algorithms such as PSO, ACO, experimental results show that the global search capability of IPSO has been significantly improved and IPSO can effectively avoid premature convergence problem. Also it can solve the multi-agent coalition formation problem effectively and efficiently.

A Novel Distributed Fuzzy-based Negotiation Model for Coalition Formation in Multi-Agent Systems

A Novel Distributed Fuzzy-based Negotiation Model for Coalition Formation in Multi-Agent Systems

Trust-based coalition formation in multi-agent systems

In this paper, we provide a framework to study trust-based coalition formation in multi-agent systems using cooperative game theory as the underlying mathematical framework. We describe how to study trust dynamics between agents as a result of their trust synergy and trust liability in cooperative coalitions. We also rigorously justify the behaviors of agents for different classes of games and discuss how to exploit the formal properties of these games for cooperative control in an unmanned military vehicle convoy.

Algorithms for Transitive Dependence-Based Coalition Formation

Coalition formation methods allow autonomous agents to join together in order to act as a coherent group in which they increase their individual gains by collaborating with each other. Although there are some research efforts toward coalition formation in multiagent systems (MAS), such as game theory-based approaches, these methods cannot be easily applied in real-world scenarios. Based on a novel social reasoning theory, namely, transitive dependence theory, this work proposes two dynamic coalition formation algorithms for coalition formation: 1) without and-action dependence and 2) with and-action dependence, respectively. While most related work addresses the problem of searching for the optimal coalition structure (CS), the proposed algorithms aim to find out the optimal coalitions for specific goals. Theoretical analysis and experimental results suggest that 1) the algorithm for coalition formation without and-action dependence is of polynomial complexity and is efficient, and 2) when the incidence rate of and-action dependence is not high, the anytime algorithm for coalition formation with and-action dependence is also efficient although it has relatively high complexity (NP-complete).

A Coalition Formation Framework Based on Transitive Dependence

Coalition formation in multi-agent systems (MAS) is becoming increasingly important as it increases the ability of agents to execute tasks and maximize their payoffs. Dependence relations are regarded as the foundation of coalition formation. This paper proposes a novel dependence theory namely transitive dependence theory for dynamic coalition formation in multi-agent systems. Transitive dependence is an extension of direct dependence that supports an agent's reasoning about other social members during coalition formation. Based on the proposed transitive dependence theory, a dynamic coalition formation framework has been worked out which includes information gathering, transitive dependence based reasoning for coalition partners search and coalition resolution. The nested coalitions and how to deal with incomplete knowledge while forming coalitions are also discussed in the paper.