The concept of knowledge management has become a critical issue in both academic and practical area. Organizations invest huge resources in knowledge management work in order to maintain long term competitive advantage. Therefore, how to use information technology to support knowledge management in an efficient way is a critical success factor in organizations adopting knowledge management. However, the running effect of knowledge management system does not achieve organizations¡¦ expectancy. The main reason is that knowledge management system is designed for unstable form of knowledge; developing information system in a structural way can not process this abstract knowledge effectively. For the reason, this research applies intelligent agent technique in developing knowledge management system, hoping to make use of intelligent agents¡¦ autonomy, communication ability, adaptability and mobility to raise the efficiency of knowledge management. This research analysis the general knowledge management works in order to find out the general knowledge management requirements. And then, this research also checks which requirements are suitable for intelligent agent to process. After integrating these requirements, this research proposes a complete intelligent agent based knowledge management system framework and a detail definition of each intelligent agent, and a set of message communication protocols between these intelligent agents.

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Kidney transplantation is an effective treatment for renal disease that was previously fatal. However, the demand for donor kidneys far exceeds the supply. Due to the scarcity of volunteer donors, the cadaver organs that are retrieved must be optimally utilized. By expanding organ retrieval and sharing pools and improving donor-patient matching algorithms, the utilization of donated organs is enhanced and encouraging medical results are obtained. However, the benefits of enlarged donor and recipient pools may be offset by increasing complexity and decreasing efficiency in the organ distribution process thus increasing cold ischemia time. It is critical to improve distribution process efficiency in order to minimize the time taken to complete the entire process, and thus further enhance patient and graft survival. I attempt to apply supply chain management concepts, agent technologies, mobile communication technologies and decision-making theory to improve the efficiency of the cadaver kidney distribution process. In this thesis I analyze what are the bottlenecks in current cadaver kidney distribution and investigate how agent technology can be applied to improve this process. I propose a distributed multi-agent system operating in a mobile and wireless communication environment to assist transplant coordinators in coordinating with multi-parties in this time-critical distribution process. A prototype system has been developed to help transplanting coordinators in allocating the kidney recipient. / Thesis / Master of Science (MS)

ITC/USA 2011 Conference Proceedings / The Forty-Seventh Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2011 / Bally's Las Vegas, Las Vegas, Nevada / This paper describes a service-based Intelligent Agent (IA) approach for machine learning and data mining of distributed heterogeneous data streams. We focus on an open architecture framework that enables the programmer/analyst to build an IA suite for mining, examining and evaluating heterogeneous data for semantic representations, while iteratively building the probabilistic model in real-time to improve predictability. The Framework facilitates model development and evaluation while delivering the capability to tune machine learning algorithms and models to deliver increasingly favorable scores prior to production deployment. The IA Framework focuses on open standard interoperability, simplifying integration into existing environments.

This dissertation addresses improvements to a consensus based task allocation algorithms for improving the Quality of Service in multi-task and multi-agent environments. Research in the past has led to many centralized task allocation algorithms where a central computation unit is calculating the global optimum task allocation solution. The centralized algorithms are plagued by creating a single point of failure and the bandwidth needed for creating consistent and accurate situational awareness off all agents. This work will extend upon a widely researched decentralized task assignment algorithm based on the consensus principle. Although many extensions have led to improvements of the original algorithm, there is still much opportunity for improvement in providing sufficient and reliable task assignments in real-world dynamic conditions and changing environments. This research addresses practical changes made to the consensus based task allocation algorithms for improving the Quality of Service in multi-task and multi-agent environments.

The dissertation proposed an approach, named "Signal Optimization Under Rail Crossing sAfety cOnstraints"(SOURCAO), to the traffic signal control near a highway rail grade crossing (HRGC). SOURCAO targets two objectives: HRGC safety improvement (a high priority national transportation goal) and highway traffic delay reduction (a common desire for virtually all of us). Communication and data availability from ITS and the next generation train control are assumed available in SOURCAO. The first step in SOURCAO is to intelligently choose a proper preemption phase sequence to promote HRGC safety. An inference engine is designed in place of traditional traffic signal preemption calls to prevent the queue from backing onto HRGC. The potential hazard is dynamically examined as to whether any queuing vehicle stalls on railroad tracks. The inference engine chooses the appropriate phase sequence to eliminate the hazardous situation. The second step in SOURCAO is to find the optimized phase length. The optimization process uses the network traffic delay (close to the control delay) at the intersections within HRGC vicinities as an objective function. The delay function is approximated and represented by multilayer perceptron neural network (off-line). After the function was trained and obtained, an optimization algorithm named Successive Quadratic Programming (SQP) searches the length of phases (on-line) by minimizing the delay function. The inference engine and proposed delay model in optimization take the on-line surveillance detector data and HRGC closure information as input. By integrating artificial intelligence and optimization technologies, the independent simulation evaluation of SOURCAO by TSIS/CORSIM demonstrated that the objectives are reached. The average network delay for 20 runs of simulation evaluation is reduced over eight percent by a t-test while the safety of HRGC is promoted. The sensitivity tests demonstrate that SOURCAO works efficiently under light and heavy traffic conditions, as well as a wide range of HRGC closure times. / Ph. D.

Indiana University-Purdue University Indianapolis (IUPUI) / Over the past decade, universities had a significant improvement in using online learning tools. A standard learning management system provides fundamental functionalities to satisfy the basic needs of its users. The new generation of learning management systems have introduced a novel system that provides social networking features. An unprecedented number of users use the social aspects of such platforms to create their profile, collaborate with other users, and find their desired career path. Nowadays there are many learning systems which provide learning materials, certificates, and course management systems. This allows us to utilize such information to help the students and the instructors in their academic life. The presented research work's primary goal is to focus on creating an intelligent personal assistant within the social learning systems. The proposed personal assistant has a human-like persona, learns about the users, and recommends useful and meaningful materials for them. The designed system offers a set of features for both institutions and members to achieve their goal within the learning system. It recommends jobs and friends for the users based on their profile. The proposed agent also prioritizes the messages and shows the most important message to the user. The developed software supports model-controller-view architecture and provides a set of RESTful APIs which allows the institutions to integrate the proposed intelligent agent with their learning system.