Most research investigating how social influence affects election results mainly uses diffusion models for binary opinions. However, these diffusion models are progressive and focus on the diffusion of one opinion. In this article, we introduce a general diffusion model for ordinal opinions expressed as linear orderings over a finite set of candidates. We employ agent-based modeling to simulate a nonprogressive diffusion process, allowing multiple types of opinion diffusion about different candidates. The proposed agent-based diffusion model can forecast long-term trends of opinion diffusion in social networks by capturing voters’ personalized features and incorporating dynamic social contexts. Furthermore, we examine the possibility of affecting election outcomes by externally changing the ordinal opinions of certain vertices, i.e., campaigning. Since finding influential voters from the social network is computationally challenging, we propose a heuristic approach, i.e., backward influence rank (BIR). Experimental results demonstrate that the proposed BIR approach is superior to the classic greedy approach for campaigning by achieving a similar margin of victory to that of the greedy approach but running two orders of magnitude faster than the greedy approach did.

In multi-agent systems, agents with limited capabilities need to find a cooperation partner to accomplish complex tasks. Evaluating the trustworthiness of potential partners is vital in partner selection. Current approaches are mainly averaged-based, aggregating advisors’ information on partners. These methods have limitations, such as vulnerability to unfair rating attacks, and may be locally convergent that cannot always select the best partner. Therefore, we propose a ranking-based partner selection (RPS) mechanism, which clusters advisors into groups according to their ranking of trustees and gives recommendations based on groups. Besides, RPS is an online-learning method that can adjust model parameters based on feedback and evaluate the stability of advisors’ ranking behaviours. Experiments demonstrate that RPS performs better than state-of-the-art models in dealing with unfair rating attacks, especially when dishonest advisors are the majority.

High-performance data streaming technologies are increasingly adopted in IT companies to support the integration of heterogeneous and possibly distributed applications. Compared with the traditional message queuing middleware, a streaming platform enables the implementation of event-streaming systems (ESS) which include not only complex queues but also pipelines that transform and react to the streams of data. By analysing the centralised data streams, one can evaluate the Quality-of-Service for other systems and components that produce or consume those streams. We consider the exploitation ofprobabilistic model checkingas a performance monitoring technique for ESS systems. Probabilistic model checking is a mature, powerful verification technique with successful application in performance analysis. However, an ESS system may contain quantitative parameters that are determined by event streams observed in a certain period of time. In this paper, we present a novel theoretical framework called QV4M (meaning “quantitative verification for monitoring”) for monitoring ESS systems, which is based on two recent methods of probabilistic model checking. QV4M assumes the parameters in a probabilistic system model as random variables and infers the statistical significance for the probabilistic model checking output. We also present an empirical evaluation of computational time and data cost for QV4M.

Multi-agent technologies have been widely applied to many applications, such as in e-markets, cloud computing, service-oriented environments, etc. In real applications, service-oriented environments are open and dynamic, where loosely coupled agents interact to consume and provide services. How to accurately evaluate the potential performance (i.e., QoS) of service providers on the service requested by a service consumer in such open and dynamic environments is a challenging issue in both theory and practice. In this paper, an innovative approach is proposed to evaluate the QoS of service providers in service-oriented environments. The proposed approach first borrows the reference report mechanism from the certified reputation model, so as to efficiently collect reference reports (i.e., historical performance) of service providers in open and dynamic environments. Then, a tensor-based QoS model is proposed to construct multi-dimensional relationships between QoS evaluation factors and the QoS values of service providers based on the collected reference reports. The QoS evaluation factors include the type of services, the performance of service providers, the subjectivity of service consumers, the time slot of reference reports. Finally, a CANDECOMP/PARAFAC decomposition and gradient descent-based mechanism is used to evaluate the QoS values of service providers through completing the missing entry values in the constructed tensor. The uniform random simulation experiments indicate that the proposed approach can achieve efficient and accurate QoS evaluation in service-oriented environments with only limited collected reference reports, especially when some service providers do not have reference reports.

Automated negotiations based on learning models have been widely applied in different domains of negotiation. Specifically, for resource allocation in decentralised open market environments with multiple vendors and multiple buyers. In such open market environments, there exists dynamically changing supply and demand of resources, with dynamic arrival of buyers in the market. Besides, each buyer has their own set of constraints, such as budget constraints, time constraints, etc. In this context, efficient negotiation policies should be capable of maintaining the equilibrium between the utilities of both the vendors and the buyers. In this research, we aim to design a mechanism for an optimal auction paradigm, considering the existence of interdependent undisclosed preferences of both, buyers and vendors. Therefore, learning-based negotiation models are immensely appropriate for such open market environments; wherein, self-interested autonomous vendors and buyers cooperate/compete to maximize their utilities based on their undisclosed preferences. Toward this end, we present our current proposal, the two-stage learning-based resource allocation mechanism, wherein utilities of vendors and buyers are optimised at each stage. We are aiming to compare our proposed learning-based resource allocation mechanism with two state-of-the-art bidding-based resource allocation mechanism, which are based on, fixed bidding policy (Samimi, Teimouri, and Mukhtar 2016) and demand-based bidding policy (Kong, Zhang, and Ye 2015). The comparison is to be done based on the overall performance of the open market environment and also based on the individual performances of vendors and buyers.

Autonomous driving is one of the most important AI applications and has attracted extensive interest in recent years. A large number of studies have successfully applied reinforcement learning techniques in various aspects of autonomous driving, ranging from low-level control of driving maneuvers to higher level of strategic decision-making. However, comparatively less progress has been made in investigating how co-existing autonomous vehicles would interact with each other in a common environment and how reinforcement learning can be helpful in such situations by applying multiagent reinforcement learning techniques in the high-level strategic decision-making of the following or overtaking for a group of autonomous vehicles in highway scenarios. Learning to achieve coordination among vehicles in such situations is challenging due to the unique feature of vehicular mobility, which renders it infeasible to directly apply the existing coordinated learning approaches. To solve this problem, we propose using dynamic coordination graph to model the continuously changing topology during vehicles' interactions and come up with two basic learning approaches to coordinate the driving maneuvers for a group of vehicles. Several extension mechanisms are then presented to make these approaches workable in a more complex and realistic setting with any number of vehicles. The experimental evaluation has verified the benefits of the proposed coordinated learning approaches, compared with other approaches that learn without coordination or rely on some traditional mobility models based on some expert driving rules.

Influence diffusion modelling, analysis and applications in the preference-aware context draw tremendous attention to both researchers and practitioners. Most contemporary studies typically model the influence-diffusion pheromone from a centralized perspective. In this paper, we model the bi-directional influence propagation in directed weighted networks in a distributed manner with the consideration of user preference by facilitating Agent-Based Modelling. In the proposed model, each individual’s personalized features and the social context are modelled based on the underlying social theories, i.e. social influence and the homophily effect. In addition, the model is capable of not only producing a certain range of dynamical behaviours based on different parameter constellation but also analyzing the evolutionary trends of a social network and capturing the dynamics in the environment. Another attractive feature is the training capability of agents, which enables them to adapt the personalized features. Comparing with traditional approaches, the proposed model is more suitable for handling the complex nature of influence diffusion, and demonstrates the advantages in simulating the real-world influence diffusion. Furthermore, we propose a novel seeding algorithm for influence maximization, named Enhanced Evolution-Based Backward selection. The algorithm utilizes the advantages offered by the proposed agent-based model. The experimental results reveal that the algorithm is superior to those state-of-the-art algorithms for influence maximization.

This paper addresses the issue of the wireless mobile robot deployment for the ad hoc network establishment in disaster environments, which aims to maximize the important locations covered by the established ad hoc network so as to improve the performance of task allocation. In many disaster environments, the number of wireless mobile robots usually is much less than the number of important locations in the environment so that maximizing the important locations covered by the established ad hoc network is the primary objective of wireless mobile robot deployment approaches. To maximize the coverage of important locations, most of the current approaches were developed based on greedy algorithms. Due to the myopia of greedy algorithms, these approaches can only maximize the coverage of important locations of each wireless mobile robot rather than the whole network. To this end, two mathematical programming-based wireless mobile robot deployment approaches are proposed for ad hoc network establishment in disaster environments. The proposed approach can create suitable deployment locations for all wireless mobile robots in a disaster environment. The experimental results demonstrate that ad hoc networks established by the proposed approaches can cover more important locations in a disaster environment than those established by greedy algorithm-based approaches.

In disasters, many stationary tasks, such as saving survivors in debris, extinguishing fire of buildings, and so on, need first responders to complete on site. In such circumstances, wireless mobile robots are usually employed to search for tasks and establish ad hoc networks to assist first responders. Due to the unknown and complexity of environments and limited capabilities of wireless mobile robots, searching and establishing ad hoc networks in disaster environments is a challenging issue in both theory and practice. To this end, a task-based wireless mobile robot deployment approach is proposed in this article. The proposed approach consists of a search process and a deployment process. The search process can guide wireless mobile robots to efficiently find tasks in unknown and complex environments. The deployment process can find suitable deployment locations for wireless mobile robots to establish ad hoc networks. The established ad hoc networks can ensure the communication of wireless mobile robots in the network and can cover the maximum number of task locations and the maximum areas in a disaster environment. Experimental results demonstrate that based on the proposed approach, wireless mobile robots have better performance in terms of search and ad hoc network establishment in disaster environments.

With the widespread of the Internet, the on-line social network with big data is rapidly developing over time. Many enterprises attempt to develop their business by utilizing the power of on-line social networking platforms. A considerable amount of work has focused on how to select a set of influential users to maximize a kind of positive influence in static social networks. However, networks evolve, and the topological structure changes over time. How to mine and adapt the influencers in a dynamic and large-scale environment becomes a challenging issue. In this paper, a collective intelligence model, i.e., stigmergy-based influencers miner, is proposed to investigate influential nodes in a fully dynamic environment. The proposed model is capable of analysing influential relationships in a social network in decentralized manners and identifying the influencers more efficiently than traditional seed selection algorithms. Moreover, it is capable of adapting the solutions in complex dynamic environments without any interruptions or recalculations. Experimental results show that the proposed model achieves better performance than other traditional models in both static and dynamic social networks by considering both efficiency and effectiveness.

Social influence modelling and maximization appear significant in various domains, such as e-business, marketing, and social computing. Most existing studies focus on how to maximize positive social impact to promote product adoptions based on static network snapshots. Such approaches can only increase influence in a social network in short-term, but cannot generate sustainable or long-term effects. In this research work, we study how to maintain long-term influence in a social network and propose an agent-based influence maintenance model, which can select influential nodes based on the current status in dynamic social networks in multiple times. Within the context of our investigation, the experimental results indicate that multiple-time seed selection is capable of achieving more constant impact than that of one-shot selection. We claim that influence maintenance is crucial for supporting, enhancing, and assisting long-term goals in business development. The proposed approach can automatically maintain long-lasting impact and achieve influence maintenance.

Load forecasting has been deeply studied because of its critical role in Smart Grid. In current Smart Grid, there are various types of customers with different energy consumption patterns. Customer's energy consumption patterns are referred to as customer behaviors. It would significantly benefit load forecasting in a grid if customer behaviors could be taken into account. This paper proposes an innovative method that aggregates different types of customers by their identified behaviors, and then predicts the load of each customer cluster, so as to improve load forecasting accuracy of the whole grid. Sparse Continuous Conditional Random Fields (sCCRF) is proposed to effectively identify different customer behaviors through learning. A hierarchical clustering process is then introduced to aggregate customers according to the identified behaviors. Within each customer cluster, a representative sCCRF is fine-tuned to predict the load of its cluster. The final load of the whole grid is obtained by summing the loads of each cluster. The proposed method for load forecasting in Smart Grid has two major advantages. 1) Learning customer behaviors not only improves the prediction accuracy but also has a low computational cost. 2) sCCRF can effectively model the load forecasting problem of one customer, and simultaneously select key features to identify its energy consumption pattern. Experiments conducted from different perspectives demonstrate the advantages of the proposed load forecasting method. Further discussion is provided, indicating that the approach of learning customer behaviors can be extended as a general framework to facilitate decision making in other market domains.

Predictions of time-series are widely used in different disciplines. We propose CoR, Sparse Gaussian Conditional Random Fields (SGCRF) on top of Recurrent Neural Networks (RNN), for problems of this kind. CoR gains advantages from both RNN and SGCRF. It can not only effectively represent the temporal correlations in observed data, but can also learn the structured information of the output. CoR is challenging to train because it is a hybrid of deep neural networks and densely-connected graphical models. Alternative training can be a tractable way to train CoR, and furthermore, an end-to-end training method is proposed to train CoR more efficiently. CoR is evaluated by both synthetic data and real-world data, and it shows a significant improvement in performance over state-of-the-art methods.

Summary In market‐based cloud environments, both resource consumers and providers are self‐interested; additionally, they can come and leave the environment freely. Therefore, the environment is competitive and uncertain. Because of the competition, participants may cheat in making deals, and this represents that the environment is insecure to resource providers who intend to earn profits through renting their resources to the tasks of resource consumers. Against this, in this paper, intelligent agents are designed to strategically quote for the tasks that they are interested in, on behalf of resource providers. Agents could quote according to the messages it obtained and the information learnt and predicted from the messages, to minimize the influence of insecure factors, such as cheating, competition, and dynamism. The experimental evaluation shows that the proposed method outperforms both a well‐known multiresource negotiation‐based task allocation method and a max‐sum belief propagation–based method.

Sleep/wake-up scheduling is one of the fundamental problems in wireless sensor networks, since the energy of sensor nodes is limited and they are usually unrechargeable. The purpose of sleep/wake-up scheduling is to save the energy of each node by keeping nodes in sleep mode as long as possible (without sacrificing packet delivery efficiency) and thereby maximizing their lifetime. In this paper, a self-adaptive sleep/wake-up scheduling approach is proposed. Unlike most existing studies that use the duty cycling technique, which incurs a tradeoff between packet delivery delay and energy saving, the proposed approach, which does not us duty cycling, avoids such a tradeoff. The proposed approach, based on the reinforcement learning technique, enables each node to autonomously decide its own operation mode (sleep, listen, or transmission) in each time slot in a decentralized manner. Simulation results demonstrate the good performance of the proposed approach in various circumstances.

Concurrent multiple negotiation (CMN) provides a mechanism for an agent to simultaneously conduct more than one negotiation. There may exist different interdependency relationships among these negotiations and these interdependency relationships can impact the outcomes of these negotiations. The outcomes of these concurrent negotiations contribute together for the agent to achieve an overall negotiation goal. Handling a CMN while considering interdependency relationships among multiple negotiations is a challenging research problem. This paper: 1) comprehensively highlights research problems of negotiations at concurrent negotiation level; 2) provides a graph-based CMN model with consideration of the interdependency relationships; and 3) proposes a colored Petri net-based negotiation protocol for conducting CMNs. With the proposed protocol, a CMN can be efficiently and concurrently processed and negotiation agreements can be efficiently achieved. Experimental results indicate the effectiveness and efficiency of the proposed protocol in terms of the negotiation success rate, the negotiation time and the negotiation outcome.

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.

To solve the problem of group task allocation with time constraints in open and dynamic network environments, this paper proposes a decentralized combinatorial auction-based approach for group task allocation. In the proposed approach, both resource providers and consumers are modeled as intelligent agents. The proposed approach is decentralized, so all the agents are limited to communicating with their neighboring agents. The proposed approach also allows agents to enter and leave the network environments freely, and is robust for the dynamism and openness of the network environments. Tasks in the proposed approach have deadlines, and may need the collaboration of a group of self-interested providers. The experimental results demonstrate that the proposed approach outperforms two well-known task allocation approaches in terms of success rate of task allocation, the individual utility of the agents, the speed of task allocation and scalability.

The widespread use of web services in forming complex online applications requires service composition to cope with highly dynamic and heterogeneous environments. Traditional centralized service composition techniques are not sufficient to address the needs of applications in decentralized environments. In this paper, a stigmergic-based approach is proposed to model the decentralized service interactions and handle service composition in highly dynamic open environments. In the proposed approach, web services and resources are modeled as multiple agents. Stigmergic-based self-organization mechanisms among agents are deployed to facilitate adapting service composition. In addition, to overcome the limitations of traditional QoS-based approaches, trust measurements are deployed as a criterion for service selection. To improve the performance of the proposed stigmergic-based approach under dynamic scale-free environments, we investigate the hybridization with local search operators to consolidate adaptation, and diversity schemes are introduced to facilitate continual service adaptation. Extensive experiments show the efficiency of the proposed approach in dealing with incomplete information and dynamic factors in composing and adapting web services in open environments. The experiment results also show that the proposed approach achieves a better performance than other traditional approaches.

A broker acts as a middleman between buyers and sellers in the trading processes to achieve its profit as well as to satisfy buyer's requirements based on seller's offers. This paper proposes a broker-based optimal matching approach in the markets. The major contributions of this paper include (1) an abstract model of a broker agent, that is applicable to a broad range of market types, (2) predicting buyers and sellers' behavior by using Bayes' rule so that a broker can identify an appropriate allocation of items between buyers and sellers, and (3) an objective function and a set of constraints to help a broker to maximize its profit under consideration of buyer and seller's total satisfaction. Experimental results demonstrate the good performance of the proposed approach in terms of satisfying buyer's requirements and maximizing broker's profit.

Task allocation for heterogeneous agents in disaster environments under time, space and communication constraints is a challenging issue in both theory and practice. This paper presents a dynamic task allocation approach for such situations. The proposed approach consists of an information collection mechanism, a group task allocation mechanism and a group coordination mechanism. Initially, the information collection mechanism is applied to help agents in communication networks to reduce their communication connections and select one agent in each network as the network leader in a decentralized manner so as to facilitate the collection of information for task allocation under communication constraints. Then, the group task allocation mechanism is employed by each network leader to allocate tasks and agents in its network to groups with suitable space ranges by considering time, space and communication constraints as well as the differing capabilities of agents. During task execution, due to the dynamics of disaster environments, the original allocation (by the group task allocation mechanism) of tasks and agents in groups may be unsuitable. To achieve continuous coordination of the heterogeneous agents among groups under communication constraints, the group coordination mechanism is employed. Experimental results demonstrate that the proposed approach can have better performance than many existing approaches in terms of information collection and dynamic task allocation in disaster environments under time, space and communication constraints.

Summary This paper addresses the task allocation problem in an open, dynamic grid environments and service‐oriented environments. In such environments, both grid/service providers and consumers can be modelled as intelligent agents. These agents can leave and enter the environment freely at any time. Task allocation under time constraints becomes a challenging issue in such environments because it is difficult to apply a central controller during the allocation process due to the openness and dynamism of the environments. This paper proposes a negotiation‐based method for task allocation under time constraints in an open, dynamic grid environment, where both consumer and provider agents can freely enter or leave the environment. In this method, there is no central controller available, and agents negotiate with each other for task allocation based only on local views. The experimental results show that the proposed method can outperform the current methods in terms of the success rate of task allocation and the total profit obtained from the allocated tasks by agents under different time constraints. Copyright © 2014 John Wiley & Sons, Ltd.

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) Prisoner’s 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 Prisoner’s Dilemma game or the Snow Drift game).

Multiagent learning (MAL) is a promising technique for agents to learn efficient coordinated behaviors in multiagent systems (MASs). In MAL, concurrent multiple distributed learning processes can make the learning environment nonstationary for each individual learner. Developing an efficient learning approach to coordinate agents' behaviors in this dynamic environment is a difficult problem, especially when agents do not know the domain structure and have only local observability of the environment. In this paper, a coordinated MAL approach is proposed to enable agents to learn efficient coordinated behaviors by exploiting agent independence in loosely coupled MASs. The main feature of the proposed approach is to explicitly quantify and dynamically adapt agent independence during learning so that agents can make a trade-off between a single-agent learning process and a coordinated learning process for an efficient decision making. The proposed approach is employed to solve two-robot navigation problems in different scales of domains. Experimental results show that agents using the proposed approach can learn to act in concert or independently in different areas of the environment, which results in great computational savings and near optimal performance.

Social dilemmas have attracted extensive interest in the research of multiagent systems in order to study the emergence of cooperative behaviors among selfish agents. Understanding how agents can achieve cooperation in social dilemmas through learning from local experience is a critical problem that has motivated researchers for decades. This paper investigates the possibility of exploiting emotions in agent learning in order to facilitate the emergence of cooperation in social dilemmas. In particular, the spatial version of social dilemmas is considered to study the impact of local interactions on the emergence of cooperation in the whole system. A double-layered emotional multiagent reinforcement learning framework is proposed to endow agents with internal cognitive and emotional capabilities that can drive these agents to learn cooperative behaviors. Experimental results reveal that various network topologies and agent heterogeneities have significant impacts on agent learning behaviors in the proposed framework, and under certain circumstances, high levels of cooperation can be achieved among the agents.

Reinforcement learning has emerged as a powerful tool to compose and adapt Web services in open and dynamic environments. However, the most common applications of reinforcement learning algorithms are relatively inefficient in their use of experience data, which may affect the stability of the learning process. In particular, they make just one learning update for each interaction experience. This paper proposes two novel algorithms that aim to achieve greater data efficiency by saving experience data and using it in aggregate to make updates to the learned policy. The first algorithm introduces an offline learning scheme for service composition where the online learning task is transformed into a series of supervised learning steps. The second algorithm presents a coordination mechanism in order to enable multiple agents to learn the service composition task cooperatively. The results of our experiments show the effectiveness of the proposed algorithms compared to their online learning counterparts.

SUMMARY Multiagent learning provides a promising paradigm to study how autonomous agents learn to achieve coordinated behavior in multiagent systems. In multiagent learning, the concurrency of multiple distributed learning processes makes the environment nonstationary for each individual learner. Developing an efficient learning approach to coordinate agents’ behavior in this dynamic environment is a difficult problem especially when agents do not know the domain structure and at the same time have only local observability of the environment. In this paper, a coordinated learning approach is proposed to enable agents to learn where and how to coordinate their behavior in loosely coupled multiagent systems where the sparse interactions of agents constrain coordination to some specific parts of the environment. In the proposed approach, an agent first collects statistical information to detect those states where coordination is most necessary by considering not only the potential contributions from all the domain states but also the direct causes of the miscoordination in a conflicting state. The agent then learns to coordinate its behavior with others through its local observability of the environment according to different scenarios of state transitions. To handle the uncertainties caused by agents’ local observability, an optimistic estimation mechanism is introduced to guide the learning process of the agents. Empirical studies show that the proposed approach can achieve a better performance by improving the average agent reward compared with an uncoordinated learning approach and by reducing the computational complexity significantly compared with a centralized learning approach. Copyright © 2012 John Wiley & Sons, Ltd.

Social norms such as social rules and conventions play a pivotal role in sustaining system order by regulating and controlling individual behaviors toward a global consensus in large-scale distributed systems. Systematic studies of efficient mechanisms that can facilitate the emergence of social norms enable us to build and design robust distributed systems, such as electronic institutions and norm-governed sensor networks. This paper studies the emergence of social norms via learning from repeated local interactions in networked multiagent systems. A collective learning framework, which imitates the opinion aggregation process in human decision making, is proposed to study the impact of agent local collective behaviors on the emergence of social norms in a number of different situations. In the framework, each agent interacts repeatedly with all of its neighbors. At each step, an agent first takes a best-response action toward each of its neighbors and then combines all of these actions into a final action using ensemble learning methods. Extensive experiments are carried out to evaluate the framework with respect to different network topologies, learning strategies, numbers of actions, influences of nonlearning agents, and so on. Experimental results reveal some significant insights into the manipulation and control of norm emergence in networked multiagent systems achieved through local collective behaviors.

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.

Web services have the potential to offer the enterprises with the ability to compose internal and external business services in order to accomplish complex processes. Service composition then becomes an increasingly challenging issue when complex and critical applications are built upon services with different QoS criteria. However, most of the existing QoS -aware compositions are simply based on the assumption that multiple criteria, no matter whether these multiple criteria are conflicting or not, can be combined into a single criterion to be optimized, according to some utility functions. In practice, this can be very difficult as utility functions or weights are not well known a priori. In this paper, a novel multi-objective approach is proposed to handle QoS -aware Web service composition with conflicting objectives and various restrictions on quality matrices. The proposed approach uses reinforcement learning to deal with the uncertainty characteristic inherent in open and decentralized environments. Experimental results reveal the ability of the proposed approach to find a set of Pareto optimal solutions, which have the equivalent quality to satisfy multiple QoS -objectives with different user preferences.

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.

Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardized components rather than reinventing the wheel each time. Moreover, because these patterns are identified from a wide variety of existing negotiating agents (especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system.

Electronic Commerce has been a very significant commercial phenomenon in recent years, and autonomous agents are widely adopted by business or individuals in electronic marketplaces to fulfill time consuming tasks in trading. Agent negotiation mechan

Trust is a crucial aspect for open distributed systems. Especially as users may rely on the shared services to make important decisions, it is essential to let them know services' trustworthiness. Provenance describes the origins and processes that are related to the generation of services. It can greatly enhance transparency and accountability of shared services. In this paper, we focus on how to derive trust information from huge amount of provenance data, and proposed a case-based approach which can estimate services' trustworthiness from provenance. This approach can greatly utilise the value of provenance, and provide more objective and reasonable trust estimation to users.

Abstract Mobile agents often travel in a hostile environment where their security and privacy could be compromised by any party including remote hosts in which agents visit and get services. It is believed that the host visited by an agent should jointly sign a service agreement with the agent's owner; hence a proxy‐signing model was proposed in the literature, allowing every host in the agent system to sign a service agreement. We observe that this actually poses a serious problem whereby a host that should be excluded from an underlying agent network could also send a signed service agreement. In order to solve this problem, we propose two schemes achieving host authentication with controlled resources, where only selected hosts can be included in the agent network. We provide two schemes in this paper. The second scheme offers a smaller data size. We also define security models and provide rigorous security proofs to our schemes. Copyright © 2010 John Wiley & Sons, Ltd.

How to choose the best service provider (agent), which a service consumer can trust in terms of the quality and success rate of the service in an open and dynamic environment, is a challenging problem in many service-oriented applications such as Internet-based grid systems, e-trading systems, as well as service-oriented computing systems. This paper presents a Priority-Based Trust (PBTrust) model for service selection in general service-oriented environments. The PBTrust is robust and novel from several perspectives. (1) The reputation of a service provider is derived from referees who are third parties and had interactions with the provider in a rich context format, including attributes of the service, the priority distribution on attributes and a rating value for each attribute from a third party, (2) The concept of 'Similarity' is introduced to measure the difference in terms of distributions of priorities on attributes between requested service and a refereed service in order to precisely predict the performance of a potential provider on the requested service, (3) The concept of general performance of a service provider on a service in history is also introduced to improve the success rate on the requested service. The experimental results can prove that PBtrust has a better performance than that of the CR model in a service-oriented environment.

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.

Mobile agents often travel in a hostile environment where their security and privacy could be compromised by any party including remote hosts in which agents visit and get services. It was proposed in the literature that the host visited by an agent should jointly sign a service agreement with the agent's home, where a proxy-signing model was deployed and every host in the agent system can sign. We observe that this actually poses a serious problem in that a host that should be excluded from an underlying agent network could also send a signed service agreement. In order to solve this problem, we propose a secure mobile agent scheme achieving host authentication with designated hosts, where only selected hosts can be included in the agent network. We also present a security model and provide a rigorous security proof to our scheme.

Metacomputing aims at using the computing power of computers linked by networks for various applications, which used to be tackled by dedicated supercomputers a few years ago. Web-based metacomputing offers a cheap and efficient solution for parallel and distributed computing by exploiting idle cycles from the Internet. Research literatures have been published to report the state of the art of metacomputing by addressing the developed prototypes – architecture, support methodologies and technologies, programming APIs, example benchmarks and experimental performances. This paper tries to answer the question: why the metacomputing system is undesirable in performance, applicability and reliability. In this paper, the system’s components are modelled as three roles: Client, Worker and Manager. Their performances are individually investigated in terms of the Theory of Constraint: Objectives, Requirements, Prerequisites, Conflicts and Injections for identifying core problems and exploring efficient solutions. The main contribution of this paper is to be a very first literature as conceptual guidelines for design and optimisation of metacomputing systems.

Fusion is one of the main technical problems of running a metasearch engine (MSE). In this article, we have classified the potential cases of fusion in MSE systems and the types of MSEs. Based on these classifications, the rational constraints of fusion methods in different fusion cases are described. This provides a reliable foundation for the construction of rational fusion algorithms in MSE systems. © 2004 Wiley Periodicals, Inc.

In this paper, firstly, potential synthesis cases in distributed expert systems (DESs) and types of DESs are identified. Based on these results, necessary conditions of synthesis strategies in different synthesis cases are recognized. Secondly, two methodologies for designing synthesis strategies in DESs are investigated. They are analysis methods and inductive methods. Thirdly, two methodologies are discussed based on the viewpoints of performance, complexity and requirements.