A Machine Learning Approach to Automated Negotiation

Automated negotiation between two competitive agents is analyzed, and a multi-issue negotiation model based on machine learning, time belief, offer belief and state-action pair expected Q value is developed. Unlike the widely used approaches such as game theory approach, heuristic approach and argumentation approach, This paper uses a machine learning method to compute agents‘ average Q values in each negotiation stage. The delayed reward is used to generate agents‘ offer and counteroffer of every issue. The effect of time and discount rate on negotiation outcome is analyzed. Theory analysis and experimental data show this negotiation model is practical.     

DDGrid： a grid computing system for drug discovery and design

0IntroductionCurrently,most applicationsinthe field of drug dis-covery and design are running on high throughput clus-ters.Utilizing these clusters,scientists can run theircompute-intensive and data-intensive applications such asmolecular docking and quantum mechanics in parallelmode and get results efficiently.However,with more ap-plications submitted to a cluster,the workload becomesmuch heavier and the efficiency becomes much lower.Acommon solutionisto upgrade the computing power of theclus…     

A Chord-based resource scheduling approach in drug discovery grid

This paper presents a resource scheduling approach in grid computing environment. Using P2P technology, this novel approach can schedule dynamic grid computing resources efficiently. Grid computing resources in different domains are organized into a structured P2P overlay network. Available resource information is published in type of grid services. Task requests for computational resources are also presented as grid services. Problem of resources scheduling is translated into services discovery. Different from central scheduling approaches that collect available resources information, this Chord-based approach forwards task requests in the overlay network and discovers satisfied resources for these tasks. Using this approach, the computational resources of a grid system can be scheduled dynamically according to the real-time workload on each peer. Furthermore, the application of this approach is introduced into DDG, a grid system for drug discovery and design, to evaluate the performance. Experimental results show that computational resources of a grid system can be managed efficiently, and the system can hold a perfect load balance state and robustness.     

中国教育和科研网华东南和上海地区网首期工程总体设计

中国教育和科研计算机网（ＣＥＲＮＥＴ）是全国范围的教育和科研基础设施，它由全国主干网、地区网和校园网组成，ＣＥＲＮＥＴ的建成将极大地推动我国教育、科研和经济的发展．介绍了华东南地区和上海市地区网第一期工程的设计思想和内容．     

DEAON： dynamically-evolving active overlay networks for scalable information retrieval

In this paper,we propose the dynamically-evolving active overlay network(DEAON),which is an efficient,scalable yet simple protocol to facilitate applications of decentralized information retrieval in P2P networks.DEAON consists of three novel components:a Desirable Topology Construction and Adantation algorithm to guide the evolution of the overlay topology towards a small-world-like graph;a Semantic-based Neighbor Selection scheme to conduct an online neighbor ranking;a Topology-aware Intelligent Search mechanism to forward incoming queries to deliberately selected neighbors.We deploy and compare DEAON with other several existing distributed search techniques over static and dynamic environments.The results indicate that DEAON outperforms its competitors by achieving higher recall rate while using much less network resources,in both of the above environments.     

济南市社会经济与生态环境协调发展演进分析与对策

以济南市为研究对象，选取社会经济与生态环境评价指标，建立评价标准与模型，对社会经济与生态环境的协调度进行了分析和评价．最后提出了社会经济与生态环境协调发展的对策．