Dayong Ye

A belief propagation-based method for task allocation in open and dynamic cloud environments

Abstract We propose a decentralized belief propagation-based method, PD-LBP, for multi-agent task allocation in open and dynamic grid and cloud environments where both the sets of agents and tasks constantly change. PD-LBP aims at accelerating the online response to, improving the resilience from the unpredicted changing in the environments, and reducing the message passing for task allocation. To do this, PD-LBP devises two phases, pruning and decomposition. The pruning phase focuses on reducing the search space through pruning the resource providers, and the decomposition addresses decomposing the network into multiple independent parts where belief propagation can be operated in parallel. Comparison between PD-LBP and two other state-of-the-art methods, Loopy Belief Propagation-based method and Reduced Binary Loopy Belief Propagation based method, is performed. The evaluation results demonstrate the desirable efficiency of PD-LBP from both the shorter problem solving time and smaller communication requirement of task allocation in dynamic environments.

Self-Adaptation-Based Dynamic Coalition Formation in a Distributed Agent Network: A Mechanism and a Brief Survey

In some real systems, e.g., distributed sensor networks, individual agents often need to form coalitions to accomplish complex tasks. Due to communication and computation constraints, it is infeasible for agents to directly interact with all other agents to form coalitions. Most previous coalition formation studies, however, overlooked this aspect. Those studies did not provide an explicitly modeled agent network or assumed that agents were in a fully connected network, where an agent can directly communicate with all other agents. Thus, to alleviate this problem, it is necessary to provide a neighborhood network structure, within which agents can directly interact only with their neighbors. Toward this end, in this paper, a self-adaptation-based dynamic coalition formation mechanism is proposed. The proposed mechanism operates in a neighborhood agent network. Based on self-adaptation principles, this mechanism enables agents to dynamically adjust their degrees of involvement in multiple coalitions and to join new coalitions at any time. The self-adaptation process, i.e., agents adjusting their degrees of involvement in multiple coalitions, is realized by exploiting a negotiation protocol. The proposed mechanism is evaluated through a comparison with a centralized mechanism (CM) and three other coalition formation mechanisms. Experimental results demonstrate the good performance of the proposed mechanism in terms of the entire network profit and time consumption. Additionally, a brief survey of current coalition formation research is also provided. From this survey, readers can have a general understanding of the focuses and progress of current research. This survey provides a classification of the primary emphasis of each related work in coalition formation, so readers can conveniently find the most related studies.

A Hybrid Multiagent Framework With Q-Learning for Power Grid Systems Restoration

This paper presents a hybrid multiagent framework with a Q-learning algorithm to support rapid restoration of power grid systems following catastrophic disturbances involving loss of generators. This framework integrates the advantages of both centralized and decentralized architectures to achieve accurate decision making and quick responses when potential cascading failures are detected in power systems. By using this hybrid framework, which does not rely on a centralized controller, the single point of failure in power grid systems can be avoided. Further, the use of the Q-learning algorithm developed in conjunction with the restorative framework can help the agents to make accurate decisions to protect against cascading failures in a timely manner without requiring a global reward signal. Simulation results demonstrate the effectiveness of the proposed approach in comparison with the typical centralized and decentralized approaches based on several evaluation attributes.

Self-organization in an agent network: A mechanism and a potential application

Self-organization provides a suitable paradigm for developing self-managed complex computing systems, e.g., decision support systems. Towards this end, in this paper, a composite self-organization mechanism in an agent network is proposed. To intuitively elucidate this mechanism, a task allocation environment is simulated. Based on self-organization principles, this mechanism enables agents to dynamically adapt relations with other agents, i.e., to change the underlying network structure, so as to achieve efficient task allocation. The proposed mechanism utilizes a trust model to assist agents in reasoning with whom to adapt relations and employs a multi-agent Q-learning algorithm for agents to learn how to adapt relations. Moreover, in this mechanism, it is considered that the agents are connected by weighted relations, instead of crisp relations. The proposed mechanism is evaluated through a comparison with a centralized mechanism and the K-Adapt mechanism in both closed and open agent networks. Experimental results demonstrate the adequate performance of the proposed mechanism in terms of the entire network profit and time consumption. Additionally, a potential application scenario of this mechanism is also given, which exhibits the potential applicability of this mechanism in some real world cases.

A Survey of Self-Organization Mechanisms in Multiagent Systems

This paper surveys the literature over the last decades in the field of self-organizing multiagent systems. Self-organization has been extensively studied and applied in multiagent systems and other fields, e.g., sensor networks and grid systems. Self-organization mechanisms in other fields have been thoroughly surveyed. However, there has not been a survey of self-organization mechanisms developed for use in multiagent systems. In this paper, we provide a survey of existing literature on self-organization mechanisms in multiagent systems. We also highlight the future work on key research issues in multiagent systems. This paper can serve as a guide and a starting point for anyone who will conduct research on self-organization in multiagent systems. Also, this paper complements existing survey studies on self-organization in multiagent systems.

A Multi-Agent Framework for Packet Routing in Wireless Sensor Networks

Wireless sensor networks (WSNs) have been widely investigated in recent years. One of the fundamental issues in WSNs is packet routing, because in many application domains, packets have to be routed from source nodes to destination nodes as soon and as energy efficiently as possible. To address this issue, a large number of routing approaches have been proposed. Although every existing routing approach has advantages, they also have some disadvantages. In this paper, a multi-agent framework is proposed that can assist existing routing approaches to improve their routing performance. This framework enables each sensor node to build a cooperative neighbour set based on past routing experience. Such cooperative neighbours, in turn, can help the sensor to effectively relay packets in the future. This framework is independent of existing routing approaches and can be used to assist many existing routing approaches. Simulation results demonstrate the good performance of this framework in terms of four metrics: average delivery latency, successful delivery ratio, number of live nodes and total sensing coverage.

Decentralised dispatch of distributed energy resources in smart grids via multi-agent coalition formation

The energy dispatch problem is a fundamental research issue in power distribution networks. With the growing complexity and dimensions of current distribution networks, there is an increasing need for intelligent and scalable mechanisms to facilitate energy dispatch in these networks. To this end, in this paper, we propose a multi-agent coalition formation-based energy dispatch mechanism. This mechanism is decentralised without requiring a central controller or any global information. As this mechanism does not need a central controller, the single point of failure can be avoided and since this mechanism does not require any global information, good scalability can be expected. In addition, this mechanism enables each node in a distribution network to make decisions autonomously about energy dispatch through a negotiation protocol. Simulation results demonstrate the effectiveness of this mechanism in comparison with three recently developed representative mechanisms. We propose a multi-agent coalition formation-based mechanism for efficient energy dispatch in power distribution networks.This mechanism is decentralised and does not need global information.The network structure has influence on the performance of the mechanism.

A Self-Adaptive Strategy for Evolution of Cooperation in Distributed Networks

This paper studies the phenomenon of the evolution of cooperation in distributed networks by using an iterated game. An iterated game in a distributed network is a multiple round game, where in each round, a player gains a payoff by playing a game with its neighbours and updates its action based on the actions and/or payoffs of its neighbours. The interaction model between players is usually represented as a two-player, two-action (namely cooperation and defection) Prisoners Dilemma game (which is a prototypical model for interaction between selfish individuals). Many researchers have developed strategies (also called update rules) for the evolution of cooperation in distributed networks in order to enhance cooperation, i.e., to increase the proportion of cooperators. Experimental results reported in the current literature, however, have demonstrated that each of these strategies has both advantages and disadvantages. In this paper, a self-adaptive strategy is proposed for the evolution of cooperation in distributed networks, which can utilise the strengths and avoid the limitations of existing strategies. Moreover, we have a theoretical finding about the final proportion of cooperators, evolved by any pure (or deterministic) strategies, in four types of a game. This finding is independent of the initial proportion of cooperators, the topology of the network (e.g., a small-world network or a scale-free network), and the specific game (e.g., the Prisoners Dilemma game or the Snow Drift game).

An Efficient Task Allocation Protocol for P2P Multi-agent Systems

Recently, task allocation in multi-agent systems has been investigated by many researchers. Some researchers suggested to have a central controller which has a global view about the environment to allocate tasks. Although centralized control brings convenience during task allocation processes, it also has some obvious weaknesses. Firstly, a central controller plays an important role in a multi-agent system, but task allocation procedures will break down if the central controller of a system cannot work properly. Secondly, centralized multi-agent architecture is not suitable for distributed working environments. In order to overcome some limitations caused by centralized control, some researchers proposed distributed task allocation protocols. They supposed that each agent has a limited local view about its direct linked neighbors, and can allocate tasks to its neighbors. However, only involving direct linked neighbors could limit resource origins, so that the task allocation efficiency will be greatly reduced. In this paper, we propose an efficient task allocation protocol for P2P multi-agent systems. This protocol allows not only neighboring agents but also indirect linked agents in the system to help with a task if needed. Through this way, agents can achieve more efficient and robust task allocations in loosely coupled distributed environments (e.g. P2P multi-agent systems). A set of experiments are presented in this paper to evaluate the efficiency and adaptability of the protocol. The experiment result shows that the protocol can work efficiently in different situations.

Cloning, Resource Exchange, and RelationAdaptation: An Integrative Self-Organisation Mechanism in a Distributed Agent Network

Self-organisation provides a suitable paradigm for developing self-managed complex distributed systems, such as grid computing and sensor networks. In this paper, an integrative self-organisation mechanism is proposed. Unlike current related studies, which propose only a single principle of self-organisation, this mechanism synthesises the three principles of self-organisation: cloning/spawning, resource exchange and relation adaptation. Based on this mechanism, an agent can autonomously generate new agents when it is overloaded, exchange resources with other agents if necessary, and modify relations with other agents to achieve a better agent network structure. In this way, agents can adapt to dynamic environments. The proposed mechanism is evaluated through a comparison with three other approaches, each of which represents state-of-the-art research in each of the three self-organisation principles. Experimental results demonstrate that the proposed mechanism outperforms the three approaches in terms of the profit of individual agents and the entire agent network, the load-balancing among agents, and the time consumption to finish a simulation run.