Networked multiagent systems are very popular in large-scale application environments. In networked multiagent systems, the interaction structures can be shaped into the form of networks where each agent occupies a position that is determined by such agent's relations with others. To avoid collisions between agents, the decision of each agent's strategies should match its own interaction position, so that the strategies available to all agents are in line with their interaction structures. Therefore, this paper presents a novel decision-making model for networked multiagent strategies based on their interaction structures, where the set of strategies for an agent is conditionally decided by other agents within its dependence interaction substructure. With the presented model, the resulting strategies available to all agents can minimize the collisions of multiagents regarding their interaction structures, and the model can produce the same resulting strategies for the isomorphic interaction structures. Furthermore, this paper uses a multiagent citation network as a case study to demonstrate the effectiveness of the presented decision-making model.

There are two problems in the traditional crowdsourcing systems for handling complex tasks. First, decomposing complex tasks into a set of micro-subtasks requires the decomposition capability of the requesters; thus, some requesters may abandon using crowdsourcing to accomplish a large number of complex tasks since they cannot bear such heavy burden by themselves. Second, tasks are often assigned redundantly to multiple workers to achieve reliable results, but reliability may not be ensured when there are many malicious workers in the crowd. Currently, it is observed that the workers are often connected through social networks, a feature that can significantly facilitate task allocation and task execution in crowdsourcing. Therefore, this paper investigates crowdsourcing in social networks and presents a novel context-aware reliable crowdsourcing approach. In our presented approach, the two problems in traditional crowdsourcing are addressed as follows: 1) the complex tasks can be performed through autonomous coordination between the assigned worker and his contextual workers in the social network; thus, the requesters can be exempt from a heavy computing load for decomposing complex tasks into subtasks and combing the partial results of subtasks, thereby enabling more requesters to accomplish a large number of complex tasks through crowdsourcing, and 2) the reliability of a worker is determined not only by the reputation of the worker himself but also by the reputations of the contextual workers in the social network; thus, the unreliability of transient or malicious workers can be effectively addressed. The presented approach addresses two types of social networks including simplex and multiplex networks. Based on theoretical analyses and experiments on a real-world dataset, we find that the presented approach can achieve significantly higher task allocation and execution efficiency than the previous benchmark task allocation approaches; moreover, the presented contextual reputation mechanism can achieve relatively higher reliability when there are many malicious workers in the crowd.

Globalization increasingly demands multilingual communication on the Internet, as well as in local communities. To create customized collaboration tools to support multilingual communities, the authors’ Language Grid, established ten years ago, has been improving Web-based services to communities throughout the world by providing highly adaptable infrastructure and access to a wide variety of language resources.

With the development of service-oriented computing environments, QoS-aware service selection has been a more and more important research issue. In service composition environments, QoS attributes of atomic services are always aggregated for computing the QoS of the composite services, which has been reported in many previous studies. However, there are situations that some QoS attributes cannot be aggregated for composite services. For example, it is difficult to compute the translation quality of a composite translation service by simply aggregating its component atomic services (machine translation service, morphological analysis service, dictionary service). Moreover, when multiple QoS attributes are used for evaluating services, it is always difficult to maximize all the QoS attributes because there might be anti-correlated relations between them. To address above problems, this paper proposes an approach for selecting services based on context-aware factors of QoS attributes. In our proposed approach, context-aware factors that affect QoS attributes are first extracted from analyzing their correlation with QoS attributes. Then, QoS data is generated based on the extracted factors for QoS prediction and evaluation. Further, dynamic service selection is realized based on QoS prediction and evaluation considering user requirements. We use a case study in the domain of language service with some experiments to show the effectiveness of our approach.

Service grid is an infrastructure for service-oriented collective intelligence. It provides a set of enabling functionalities to support coordination of services, such as service registries, service composition, access control, and monitoring. To form the service-oriented collective intelligence, various types of services need to be connected on the service grid, and managed by the service grid operator. However, it is difficult for single service grid operator to gather and organize services in various domains. Therefore, building service grids in different domains and connecting these service grids are essential for expanding service-oriented collective intelligence across domains. To this end, we have designed a service domain model to specialize general-purpose service grid to a specific domain and realize interoperability among service grids. Moreover, we have also developed service grid federation architecture to share service registries, compose services across service grids, and control and monitor accesses to the composite services. Finally, we have applied the proposed architecture to the language service domain to construct the Language Grid.

Flexibility, adaptation and distribution have been regarded as major challenges of modern interorganizational workflow. To address these issues, this paper proposes an interorganizational workflow execution framework based on process agents and ECA rules. In our framework, an interorganizational workflow is modeled as a multiagent system with a process agent for each organization. The whole execution is divided into two parts: the intra-execution, which means execution within a same organization, and the inter-execution, which represents interaction between organizations. For intra-execution, we use the method of transforming the graph-based local workflow into block-based workflow to design general ECA rules. ECA rules are used to control internal state transitions and process agents are used to control external state transitions of tasks in the local workflows. Inter-execution is realized by process agent interaction protocols. The proposed approach can provide flexible execution of interorganizational workflow with distributed organizational autonomy and adaptation. A case study of offshore software development is illustrated for the proposed approach.