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

Execution performance is critical for large-scale and data-intensive workflows. This paper proposes DISWOP, a novel scheduling algorithm for data-intensive workflow optimizations; it consists of three main steps: workflow process generation, task & resource mapping, and task clustering. To evaluate the effectiveness and efficiency of DISWOP, a comparison evaluation of different workflows is conducted a prototype workflow platform. The results show that DISWOP can speed up execution performance by about 1.6-2.3 times depending on the task scale.