EXPERT DISCOVERY AND KNOWLEDGE MINING IN COMPLEX MULTI-AGENT SYSTEMS

Complex problem solving requires diverse expertise and multiple techniques. In order to solve such problems, complex multi-agent systems that include both of human experts and autonomous agents are required in many application domains. Most complex multi-agent systems work in open domains and include various heterogeneous agents. Due to the heterogeneity of agents and dynamic features of working environments, expertise and capabilities of agents might not be well estimated and presented in these systems. Therefore, how to discover useful knowledge from human and autonomous experts, make more accurate estimation for experts＇ capabilities and find out suitable expert（s） to solve incoming problems （＂Expert Mining＂） are important research issues in the area of multi-agent system. In this paper, we introduce an ontology-based approach for knowledge and expert mining in hybrid multi-agent systems. In this research, ontologies are hired to describe knowledge of the system. Knowledge and expert mining processes are executed as the system handles incoming problems. In this approach, we embed more self-learning and self-adjusting abilities in multi-agent systems, so as to help in discovering knowledge of heterogeneous experts of multi-agent systems.

Knowledge Mining:A Cross-disciplinary Survey

Knowledge mining is a widely active research area across disciplines such as natural language processing(NLP), data mining(DM), and machine learning(ML). The overall objective of extracting knowledge from data source is to create a structured representation that allows researchers to better understand such data and operate upon it to build applications. Each mentioned discipline has come up with an ample body of research, proposing different methods that can be applied to different data types. A significant number of surveys have been carried out to summarize research works in each discipline. However, no survey has presented a cross-disciplinary review where traits from different fields were exposed to further stimulate research ideas and to try to build bridges among these fields.In this work, we present such a survey.

Knowledge Mining of Low Specific Speed Centrifugal Pump Impeller Based on Proper Orthogonal Decomposition Method

To clarify the complex relation between the pump blade shape and its corresponding hydraulic performance,the knowledge mining method of centrifugal pump impeller based on proper orthogonal decomposition(POD)was proposed.The pump blade shape was parameterized by cubic Bezier curve.The Latin hypercube design method was employed to supply the necessary samples for producing the perturbations of blade wrap angle,and blade angle at inlet and outlet.The hydraulic efficiency and head were optimized by NSGA-II and RBF hybrid algorithm.The Pareto-optimal solutions were obtained.In order to further illustrate the relationship between the centrifugal pump blade shape and its hydraulic performance,the POD method was used to discover the effects of optimized blade shape to the flow solutions.For the optimization of centrifugal pump MH48-12.5,blade shape and relative velocity field in impeller from Pareto-optimal solutions were analyzed.The results demonstrate that larger blade angle and smaller wrap angle increase the average kinetic energy in impeller,resulting in higher pump head design.Smaller blade angle and larger wrap angle decrease the velocity gradient from the pressure side to suction side,resulting in smaller hydraulic loss and higher efficiency design.

CALL FOR PAPERS Workshop on Intelligence and Security Informatics (WISI’06) in conjunction with the Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD’06)

Important Dates Submission due November 15, 2005 Notification of acceptance December 30, 2005 Camera-ready copy due January 10, 2006 Workshop Scope Intelligence and Security Informatics (ISI) can be broadly defined as the study of the development and use of advanced information technologies and systems for national and international security-related applications. The First and Second Symposiums on ISI were held in Tucson,Arizona,in 2003 and 2004,respectively. In 2005,the IEEE International Conference on ISI was held in Atlanta,Georgia. These ISI conferences have brought together academic researchers,law enforcement and intelligence experts,information technology consultant and practitioners to discuss their research and practice related to various ISI topics including ISI data management,data and text mining for ISI applications,terrorism informatics,deception detection,terrorist and criminal social network analysis,crime analysis,monitoring and surveillance,policy studies and evaluation,information assurance,among others. We continue this stream of ISI conferences by organizing the Workshop on Intelligence and Security Informatics (WISI’06) in conjunction with the Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD’06). WISI’06 will provide a stimulating forum for ISI researchers in Pacific Asia and other regions of the world to exchange ideas and report research progress. The workshop also welcomes contributions dealing with ISI challenges specific to the Pacific Asian region.

Use of Data Mining to Support the Development of Knowledge Intensive CAD

In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.

KDD中规则提取的收敛网络方法及其应用

提出一种新的基于神经网络的规则提取方法 .提出的网络由一个主网络及其映射网络组成 ,具有二次收敛过程 .通过主网络的学习 (第 1次收敛 )完成知识学习和网络构造 ,在此基础上构造了其网络映射 ,通过该映射网络的收敛过程实现规则的提取 .该方法在规则提取时无须遍历解空间 ,从而很好地提高了搜索效率 ,降低了计算复杂度 .同时 ,还提出估计规则数下限的信度差方法 .

Call for Papers Special Issue of Tsinghua Science and Technology on Data Mining and Knowledge Discovery

Tsinghua Science and Technology is founded and published since 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, and other information technology fields. It is indexed by Ei and other abstracting and indexing services. From 2013, the journal commits to the open access at IEEE Xplore Digital Library.

clusterProfiler 4.0:A universal enrichment tool for interpreting omics data

Functional enrichment analysis is pivotal for interpreting highthroughput omics data in life science.It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible.To meet these requirements,we present here an updated version of our popular Bioconductor package,clusterProfiler 4.0.This package has been enhanced considerably compared with its original version published 9 years ago.The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases.It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization.Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists.We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.