Dynamic Coalition Formation Research Articles

Dynamic Coalition Formation among IoT Service Providers: A Systematic Exploration of IoT Dynamics Using an Agent-Based Model.

This paper introduces an Agent-Based Model (ABM) designed to investigate the dynamics of the Internet of Things (IoT) ecosystem, focusing on dynamic coalition formation among IoT Service Providers (SPs). Drawing on insights from our previous research in 5G network modeling, the ABM captures intricate interactions among devices, Mobile Network Operators (MNOs), SPs, and customers, offering a comprehensive framework for analyzing the IoT ecosystem's complexities. In particular, to address the emerging challenge of dynamic coalition formation among SPs, we propose a distributed Multi-Agent Dynamic Coalition Formation (MA-DCF) algorithm aimed at enhancing service provision and fostering collaboration. This algorithm optimizes SP coalitions, dynamically adjusting to changing demands over time. Through extensive experimentation, we evaluate the algorithm's performance, demonstrating its superiority in terms of both payoff and stability compared to three classical coalition formation algorithms: static coalition, non-overlapping coalition, and random coalition. This study significantly contributes to a deeper understanding of the IoT ecosystem's dynamics and highlights the potential benefits of dynamic coalition formation among SPs, providing valuable insights and opening future avenues for exploration.

Dynamic Overlapping Coalition Formation in Electricity Markets: An Extended Formal Model

The future power system will be characterized by many small decentralized power plants—so-called distributed energy resources (DERs). The integration of these DERs is vital from an economic and grid operation point of view. One approach to this is the aggregation of such DERs. The formation of coalitions as an aggregation method has already been examined in the literature and applied in virtual power plants, active distribution networks, and microgrids. The spread of DERs also increases the need for flexibility and dynamics in the power grid. One approach to address this can be overlapping coalitions. Therefore, in this paper, we first performed an analysis of related work and, in this context, found no work on overlapping coalitions for energy use cases in the literature. We then described a method for dynamic coalition formation, called dynamic coalition in electricity markets (DYCE), and analyzed how DYCE would need to be extended to include overlapping coalition formation. The extension includes the phases of product portfolio optimization and the actual coalition formation. Our analysis of DYCE shows that the methods used for the optimization of the DYCE sub-tasks are not suitable for overlapping coalitions and would have to be replaced by other methods in order to be able to form overlapping coalitions.

Causes of Stability in Dynamic Coalition Formation

We study the formation of stable outcomes via simple dynamics in cardinal hedonic games, where the utilities of agents change over time depending on the history of the coalition formation process. Specifically, we analyze situations where members of a coalition decrease their utility for a leaving agent (resent) or increase their utility for a joining agent (appreciation). We show that in contrast to classical dynamics, for resentful or appreciative agents, dynamics are guaranteed to converge under mild conditions for various stability concepts. Thereby, we establish that both resent and appreciation are strong stability-driving forces.

A discrete particle swarm optimization coalition formation algorithm for multi-pursuer multi-evader game

Multi-Pursuers Multi-Evader Game (MPMEG) is considered as a multi-agent complex problem in which the pursuers must perform the capture of the detected evaders according to the temporal constraints. In this paper, we propose a metaheuristic approach based on a Discrete Particle Swarm Optimization in order to allow a dynamic coalition formation of the pursuers during the pursuit game. A pursuit coalition can be considered as the role definition of each pursuer during the game. In this work, each possible coalition is represented by a feasible particle’s position, which changes the concerned coalition according to its velocity during the pursuit game. With the aim of showcasing the performance of the new approach, we propose a comparison study in relation to recent approaches processing the MPMEG in term of capturing time and payoff acquisition. Moreover, we have studied the pursuit capturing time according to the number of used particles as well as the dynamism of the pursuit coalitions formed during the game. The obtained results note that the proposed approach outperforms the compared approaches in relation to the capturing time by only using eight particles. Moreover, this approach improves the pursuers’ payoff acquisition, which represents the pursuers’ learning rate during the task execution.

Collaborative Coded Distributed Computing Scheme: A Two-Phase Cooperative Game Approach

As a modern communication paradigm, Artificial intelligence based Internet of Things (AIoT) can provide an interactive platform across the globe to enrich the quality of networking services. With the AIoT paradigm, coded distributed computing (CDC) has recently emerged to be a promising solution to address the straggling effects in conventional distributed computing systems. In this article, we propose a novel CDC control scheme in the AIoT platform. Based on the cooperative game theory, the main challenges of our scheme are i) the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> value decision for the CDC process, and ii) edge node resource allocation for offloading tasks. Using the ideas of coalition game and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">weighted Nash social welfare solution (WNSWS)</i> , our proposed scheme is developed as a two-phase game model to achieve a mutually desirable solution. At the first phase, a dynamic coalition formation is proceeded to select the most adaptable edge nodes for the offloading subtasks. At the second phase, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">WNSWS</i> is adopted to effectively share each edge node’s computing resource. Based on the jointly design of these two cooperative games, we explore the synergy effect to optimize the CDC process. In the edge assisted distributed computing infrastructure, our reciprocal combinative approach can provide a fair-efficient solution through the sequential interactions of edge nodes and AIoT devices. In the performance evaluation, we provide extensive simulation analyses to show our scheme’s superiority by comparing with the existing baseline protocols.

Cost-effective municipal unions formation within intermediate regions under prioritized waste energy recovery

Waste-to-Energy technology is a promising approach, that should help to mitigate risk of energy crisis and to overcome problem with increasing waste generation. The European union actively promotes energy recovery from waste in its policies and limits landfilling of utilizable waste. These legally induced changes substantially affect municipalities: waste treatment becomes more expensive and complex. To react to these up-coming complications, it is beneficial to create municipal unions, focused on the cooperation in waste management operations. Such municipal unions help to lower waste treatment costs and to optimize waste collection. This paper applies general cooperative game theory approach to study how municipalities cooperate within regions of intermediate urban-rural type. Novel distributed dynamic coalition formation algorithm is used to describe process of unions’ creation. Moreover, on the basis of sampling Shapley value, suitable financial transfers within unions are proposed. The paper also presents a review of current applications of game theory to energetics, validates proposed method using the Czech Republic interregional case study and discusses the conducted sensitivity analysis. Results suggest the most suitable municipal unions for adaptation to new waste treatment legislative and demonstrate that through cooperation municipalities are able to save up to 8% of waste treatment costs.

Adaptive Coalition Formation-Based Coordinated Voltage Regulation in Distribution Networks

High penetrations of photovoltaic (PV) systems can cause severe voltage quality problems in distribution networks. This paper proposes a distributed control strategy based on the dynamic formation of coalitions to coordinate a large number of PV inverters for voltage regulation. In this strategy, a rule-based coalition formation scheme deals with the zonal voltage difference caused by the uneven integration of PV capacity. Under this scheme, PV inverters form into separate voltage regulation coalitions autonomously according to local, neighbor as well as coalition voltage magnitude and regulation capacity information. To coordinate control within each coalition, we develop a feedback-based leader-follower consensus algorithm which eliminates the voltage violations caused by the fast fluctuations of load and PV generation. This algorithm allocates the required reactive power contribution among the PV inverters according to their maximum available capacity to promote an effective and fair use of the overall voltage regulation capacity. Case studies based on realistic distribution networks and field-recorded data validate the effectiveness of the proposed control strategy. Moreover, comparison with a centralized network decomposition-based scheme shows the flexibility of coalition formation in organizing the distributed PV inverters. The robustness and generalizability of the proposed strategy are also demonstrated.

Deep Coalitional Q-Learning for Dynamic Coalition Formation in Edge Computing

Deep Coalitional Q-Learning for Dynamic Coalition Formation in Edge Computing

Information disclosure and dynamic climate agreements: Shall the IPCC reveal it all?

This paper examines the role of public information communication in dynamic self-enforcing climate agreements. We consider a framework with implicit contracts but also a dynamic coalition formation context. In a stochastic game, where the social cost of Greenhouse Gasses (GHG) is an unknown random variable, an information sender, such as the Intergovernmental Panel on Climate Change (IPCC), controls the release of verifiable information about the unknown state variable to the countries. The equilibrium communication strategy of the IPCC takes a threshold form, above which the IPCC reveals all the information available, even if it hurts the prospect of approaching the socially optimum level of emissions. The case where the IPCC remains silent, below the threshold, vanishes as the sender gets perfectly informed about the underlying social cost.

Coalition Formation Games for Coordinated Service in Realistic Small Cell Propagation Topologies

The densification of modern wireless networks into a dense ecosystem of small cells imposes challenges for the reliable service and high quality of experience of its users as it can result into severe intercell interference, especially for users scattered on the cell edges. Joint Transmission Coordinated Multipoint (JT-CoMP) is a technique that can be deployed to form cooperating clusters of transmission points, enabling them to jointly transmit data to significantly mitigate this type of interference for these users. However, JT-CoMP stresses the backhaul links and radio resources are limited, meaning that, with incautious clustering, the data rates for cell edge users may not improve, while the data rates for the non-cell edge users may severely decrease. To tackle these drawbacks, a dynamic coalition formation algorithm is proposed to form the appropriate transmission point clusters to implement JT-CoMP. Furthermore, to ensure reliable service for all the network’s users, the case where JT-CoMP is enhanced with the capability to serve users based on their selected application is examined. The proposed scheme’s adaptability and capability to increase cell edge user throughput is then tested and compared to the non JT-CoMP case, a JT-CoMP scheme with static clustering and a JT-CoMP scheme with greedy clustering for a user mobility scenario. To obtain more reliable and accurate results of the JT-CoMP deployment, physical layer parameters retrieved from a fully deterministic physical layer radio planning tool (TruNET wireless) are imported for our simulations.

Dynamics in matching and coalition formation games with structural constraints

Matching and coalition formation are fundamental aspects in the organization of many multi-agent systems. In large populations, the emergence of coalitions is often restricted by structural constraints under which agents can reorganize, e.g., local visibility or externality constraints among the agents. We study this aspect using a novel framework for dynamics with constraints within the popular domain of hedonic coalition formation games. We analyze the effects of structural constraints on the convergence of matching and coalition formation processes to stable states. Our main result are tight characterizations for the constraint structures based on which dynamic coalition formation can stabilize quickly. We show a variety of convergence results for matching and coalition formation games with different forms of locality and externality constraints. In particular, we propose and analyze a new model of graph-based visibility for coalition formation games and tightly characterize the graph structures that allow polynomial-time convergence – it can be achieved if and only if coalition formation is based on complete or star graphs.

Complementary‐based coalition formation for energy microgrids

AbstractIn recent years, the notion of electrical energy microgrids (MGs), in which communities share their locally generated power, has gained increasing interest. Typically, the energy generated comes from renewable resources, which means that its availability is variable, ie, sometimes there may be energy surpluses and at other times energy deficits. This energy variability can be ameliorated by trading energy with a connected electricity grid. However, since main electricity grids are subject to faults or other outages, it can be advantageous for energy MGs to form coalitions and share their energy among themselves. In this work, we present our model for the dynamic formation of such MG coalitions. In our model, MGs form coalitions on the basis of complementary weather patterns. Our agent‐based model, which is scalable and affords autonomy among the MGs participating in the coalition (agents can join and depart from coalitions at any time), features methods to reduce overall “discomfort” so that, even when all participating MGs in a coalition experience deficits, they can share energy so that their overall discomfort is reduced. We demonstrate the efficacy of our model by showing empirical studies conducted with real energy production and consumption data.

A mutual-selecting market-based mechanism for dynamic coalition formation

This article presents a novel market-based mechanism for a dynamic coalition formation problem backgrounded under real-time task allocation. Specifically, we first analyze the main factors of the real-time task allocation problem, and formulate the problem based on the coalition game theory. Then, we employ a social network for communication among distributed agents in this problem, and propose a negotiation mechanism for agents forming coalitions on timely emerging tasks. In this mechanism, we utilize an auction algorithm for real-time agent assignment on coalitions, and then design a mutual-selecting method to acquire better performance on agent utilization rate and task completion rate. And finally, our experimental results demonstrate that our market-based mechanism has a comparable performance in task completion rate to a decentralized approach (within 25% better on average) and a centralized dynamic coalition formation method (within 10% less on average performance).

Multiagent-Based Cooperative Control Framework for Microgrids’ Energy Imbalance

This paper proposes a cooperative control framework for the coordination of multiple microgrids. The framework is based on the multiagent system. The control framework aims to encourage the resource sharing among different autonomous microgrids and solve the energy imbalance problems by forming the microgrid coalition self-adaptively. First, the conceptual model of the integrated microgrids and the layered cooperative control framework is presented. Then, an advanced dynamic coalition formation scheme and corresponding negotiation algorithm are introduced to model the coordination behaviors of the microgrids. The proposed control framework is implemented by the Java Agent Development Framework. A loop distribution system with multiple interconnected microgrids is simulated, and the case studies are conducted to prove the efficiency of the proposed framework.

Agent-based modelling of coalition formation in energy micro-grids

In micro-grids energy is generated and consumed locally from intermittent renewable energy sources. Because of energy variability, sometimes there are energy surpluses and sometimes deficits. The ill effects of intermittency can be reduced by trading energy by means of a connected electricity grid. This work introduces an alternative solution by forming coalitions among micro-grid communities, since mainline electricity grids are subject to faults and outages. These coalitions can withstand power outages by sharing energy among them. We present a model for dynamic micro-grid coalition formation and demonstrate that the model is scalable and preserves the autonomy of individual micro-grids by enabling them to join and depart from a coalition. Our empirical studies demonstrate that our model reduces overall 'discomfort' (a notion defined in the paper), so that even when all participating micro-grids in a coalition experience deficits, they can share energy so that their overall discomfort is minimised.

ECOOP: Applying Dynamic Coalition Formation to the Power Regulation Problem in Smart Grids

In this work, we focus on one particular area of the smart grid, namely, the challenges faced by distribution network operators in securing the balance between supply and demand in the intraday market, as a growing number of load‐controllable devices and small‐scale, intermittent generators coming from renewables are expected to pervade the system. We introduce a multiagent design to facilitate coordinating the various actors in the grid. The underpinning of our approach consists of an online cooperation scheme, ECOOP, where agents learn a prediction model regarding potential coalition partners and so can respond in an agile manner to situations that are occurring in the grid, by means of negotiating and formulating speculative solutions, with respect to the estimated behavior of the system. We provide a computational characterization for our solution in terms of complexity, as well as an empirical analysis against real consumption data sets, based on the macro‐model of the Australian energy market, showing a performance improvement of about 17%.

Coalitions Improve Performance in Data Swarming Systems

We present an argument in favor of forming coalitions of peers in a data swarming system consisting of peers with heterogeneous upload capacities. In this paper, a coalition refers to a set of peers that explicitly cooperate with other peers inside the coalition via choking, piece selection, and capacity allocation strategies. Furthermore, each peer in a coalition exchanges data with peers outside its coalition via distinct choking, piece selection, and capacity allocation strategies. We first propose a simple Random Choking strategy for peers inside a coalition and develop an analytical model for studying its performance. Our model accurately predicts a coalition's performance and shows that the proposed strategy helps a coalition achieve near-optimal performance. Furthermore, our model can be easily adapted to model a BitTorrent-like swarm. We show that our Random Choking strategy significantly outperforms Tit-for-Tat and Unchoke-All strategies proposed in prior work. We also introduce a simple piece selection strategy, which significantly improves data availability within a coalition as compared to Rarest-First strategy employed in BitTorrent systems. Using cooperative game theory, we prove the existence of stable coalitions when peer population is fixed and each peer has complete information of other peers' actions and payoffs. When peers are allowed to freely join or leave coalitions, we propose a Cooperation-Aware Better Response strategy that achieves convergence of the dynamic coalition formation process. Finally, using extensive simulations, we demonstrate that forming coalitions results in significant improvements in the overall performance of a data swarm.

Coalitional game theory approach to modeling suppliers' collaboration in supply networks

Suppliers' collaboration is a new paradigm to improve the utilization of collective intelligence in supply networks. Although existing literature advocates the notion of cooperation in supply networks, there is a dearth of studies quantitatively analyzing cost and benefit of cooperation. In this study, we first develop a model for suppliers' dynamic coalition formation using coalitional game theory. The proposed cost structure influences the utility of each possible coalition and restricts the coalition size and search space for finding possible coalitions. To distribute the profit earned by cooperation in a fair manner, a criterion named Shared Capacity Index is developed according to suppliers' capacity share in the corresponding coalition. A suppliers' cooperation algorithm is then proposed to resolve possible conflicts among network members whereby each supplier is able to explore coalitional structures autonomously. The efficiency of the proposed approach is evaluated through simulation studies and compared to other solution methods, including Shapley value and Proportional Fairness. Results demonstrate that long-term cooperation among suppliers leads to enhanced average individual profit in the network.

Dynamic Coalition Formation Based on Multi-sided Negotiation

The coalition formation is an important aspect of multi-agent negotiation and cooperation. Based on the Bilateral Shapley-Value, a multi-sided eigenvalue is presented by using the reference of n-person stochastic cooperative game. It is obvious when multisided eigenvalue have superadditivity, rational agents will combine to one coalition. A dynamic coalition formation algorithm is constructed based on the eigenvalue. Procedures of multi-sided negotiation, agent’s negotiation and coalition condensing are introduced in detail. In the end, the complexity, validity, coalition stability and parameter’s function of the algorithm is given. According to these, the correctness of the algorithm is proven.

Hedonic Coalition Formation in Networks

Coalition formation is a fundamental problem in the organization of many multi-agent systems. In large populations, the formation of coalitions is often restricted by structural visibility and locality constraints under which agents can reorganize. We capture and study this aspect using a novel network-based model for dynamic locality within the popular framework of hedonic coalition formation games. We analyze the effects of network-based visibility and structure on the convergence of coalition formation processes to stable states. Our main result is a tight characterization of the structures based on which dynamic coalition formation can stabilize quickly. Maybe surprisingly, polynomial-time convergence can be achieved if and only if coalition formation is based on complete or star graphs.