7170273
Intelligent student profiling with fuzzy models
Traditional Web-based educational systems still have several shortcomings when comparing with real-life classroom teaching, such as lack of contextual and adaptive support, lack of flexible support of the presentation and feedback, lack of the collaborative support between students and systems. Based on educational theory, personalization increases learning motivation, which can increase the learning effectiveness. A fuzzy epistemic logic has been built to present the student's knowledge state, while the course content is modeled by the concept of context. By applying such fuzzy epistemic logic, the content model, the student model, and the learning plan have been defined formally. A multi-agent based student profiling system has been presented. Our profiling system stores the learning activities and interaction history of each individual student into the student profile database. Such profiling data is abstracted into a student model. Based on the student model and the content model, dynamic learning plans for individual students are made. Students get personalized learning materials, personalized quizzes, and personalized advice. In order to understand the students' perception of our prototype system and to evaluate the students' learning effectiveness, a field survey has been conducted. The results from the survey indicate that our prototype system makes great improvement on personalization of learning and achieves learning effectiveness.
7484569
The design of intelligent workflow monitoring with agent technology
In recent years, workflow technology has been widely used in business process management. With the increased complexity, uncertainty and risks in business operations, workflow monitoring is gaining growing attention in business process controlling and supervision. However, monitoring functions provided in traditional workflow systems lack flexibility, and provide little support for managing complex and dynamical changes in business process. In this paper, we propose a novel workflow monitoring approach, in which various intelligent agents work together to perform flexible monitoring tasks in an autonomous and collaborative way. By using customized monitoring plan and proactive monitoring process, the workflow monitoring activities can be executed flexibly and efficiently. The application of intelligent agents for such flexible, adaptive and collaborative workflow monitoring is investigated through an intelligent monitoring system in securities trading.
9229028
A conceptual model of personalized virtual learning environments
The Virtual Learning Environment (VLE) is one of the fastest growing areas in educational technology research and development. In order to achieve learning effectiveness, ideal VLEs should be able to identify learning needs and customize solutions, with or without an instructor to supplement instruction. They are called Personalized VLEs (PVLEs). In order to achieve PVLEs success, comprehensive conceptual models corresponding to PVLEs are essential. Such conceptual modeling development is important because it facilitates early detection and correction of system development errors. Therefore, in order to capture the PVLEs knowledge explicitly, this paper focuses on the development of conceptual models for PVLEs, including models of knowledge primitives in terms of learner, curriculum, and situational models, models of VLEs in general pedagogical bases, and particularly, the definition of the ontology of PVLEs on the constructivist pedagogical principle. Based on those comprehensive conceptual models, a prototyped multiagent-based PVLE has been implemented. A field experiment was conducted to investigate the learning achievements by comparing personalized and non-personalized systems. The result indicates that the PVLE we developed under our comprehensive ontology successfully provides significant learning achievements. These comprehensive models also provide a solid knowledge representation framework for PVLEs development practice, guiding the analysis, design, and development of PVLEs.
11019838
Intelligent Decision Support for Family Financial Planning
In today’s financial markets characterized by constantly changing tax laws and increasingly complex transactions, the demand for family financial planning (FFP) services is rising dramatically. However, the current trend to develop advisory systems that focus mainly on the financial or investment side fails to consider the whole picture of FFP. Separating financial or investment advice from legal and accounting advice may result in conflicting advice or important omissions that could lead to users suffering financial loss. In this paper, we propose a conceptual model for FFP decision-making process, followed by a novel architecture to support an aggregated FFP decision process by utilizing intelligent-agents and Web-services technology. A prototype system for supporting FFP decision is presented to demonstrate the advances of the proposed Web-service multi-agents-based system architecture and business value.
7446737
A web-service agent-based decision support system for securities exception management
Abstract With rising trading volumes and increasing risks in securities transactions, the securities industry is making an effort to shorten the trade lifecycle and minimize transaction risks. While attempting to achieve this, exception management is crucial to pass trade information within the trade lifecycle in a timely and accurate fashion. For a competitive solution to exception management, a web-service-agent-based decision support system is developed in this paper. Agent technology is applied to deal with the dynamic, complex, and distributed processes in exception management; web services techniques are proposed for more scalability and interoperability in network-based business environment. By integrating agent technology with web services to make use of the advantages from both, this approach leads to more intelligence, flexibility and collaboration in business exception management. The effectiveness of this approach is evaluated through a use case and demonstration feedback.
12991047
The Application of Adaptive Partitioned Random Search in Feature Selection Problem
Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.
2868377
Intelligent decision system and its application in business innovation self assessment
In this paper, it is described how a multiple criteria decision analysis software tool, the Intelligent Decision System (IDS), can be used to help business self-assessment. Following a brief outline of a model for assessing business innovation capability and the IDS software, the process of using IDS to implement different types of assessment questions is discussed. It is demonstrated that IDS is a flexible tool capable of handling different types of data in self-assessment, including uncertain and incomplete data, and providing a wide range of information including scores, performance diversity, strength and weakness profile and graphics.
18153606
Virtual distributed environments in a shared infrastructure
We have developed a middleware system that integrates and extends virtual machine and virtual network technologies to support mutually isolated virtual distributed environments in shared infrastructures like the grid and the PlanetLab overlay infrastructure. Integrating virtual network and on-demand virtual machine creation and customization technologies makes virtual distributed environments a reality. The Violin-based middleware system integrates and enhances such technologies to create virtual distributed environments.
17270276
ProteinDBS: a real-time retrieval system for protein structure comparison
We have developed a web server (ProteinDBS) for the life science community to search for similar protein tertiary structures in real time. This system applies computer visualization techniques to extract the predominant visual patterns encoded in two-dimensional distance matrices generated from the three-dimensional coordinates of protein chains. When meaningful contents, represented in a multi-dimensional feature space, have been extracted from distance matrices, an advanced indexing structure, Entropy Balanced Statistical (EBS) k-d tree, is utilized to index the data. Our system is able to return search results in ranked order from a database with 46 075 chains in seconds, exhibiting a reasonably high degree of precision. To our knowledge, this is the first real-time search engine for protein structure comparison. ProteinDBS provides two types of query method: query by Protein Data Bank protein chain ID and by new structures uploaded by users. The system is hosted at http://ProteinDBS.rnet.missouri.edu.
9746543
Design an Interoperable Mobile Agent System Based on Predicate Transition Net Models
Mobile agents provide an effective and flexible style to develop advanced distributed systems. In order to promote interoperability and ensure the quality of mobile agent systems, it is necessary to formalize software architecture of mobile agent systems. In this paper, we not only define the software architecture of interoperable mobile agent systems using predicate transition nets, but also analyze the interoperability between agents. In addition, a formal model-driven design method to develop mobile agent systems is described with examples. Our method naturally integrates formal methods and practical approaches in the agent system design phase. The method can be used to develop other complex software systems as well.
424382
An Efficient Computational Method for Globally Optimal Threading
Computational recognition of native-like folds of an anonymous amino acid sequence from a protein fold database is considered to be a promising approach to the three-dimensional (3D) fold prediction of the amino acid sequence. We present a new method for protein fold recognition through optimally aligning an amino acid sequence and a protein fold template (protein threading). The fitness of aligning an amino acid sequence with a fold template is measured by (1) the singleton fitness, representing the compatibility of substituting one amino acid by another and the combined preference of secondary structure and solvent accessibility for a particular amino acid, (2) the pairwise interaction, representing the contact preference between a pair of amino acids, and (3) alignment gap penalties. Though a protein threading problem so defined is known to be NP-hard in the most general sense, our algorithm runs efficiently if we place a cutoff distance on the pairwise interactions, as many of the existing threading programs do. For an amino acid sequence of size n and a fold template of size m with M core secondary structures, the algorithm finds an optimal alignment in O (Mn1.5C + 1 + mnC + 1) time and O (MnC + 1) space, where C is a (small) nonnegative integer, determined by a particular mathematical property of the pairwise interactions. As a case study, we have demonstrated that C is less than or equal to 4 for about 75% of the 293 unique folds in our protein database, when pairwise interactions are restricted to amino acids < or = 7 A apart (measured between their beta carbon atoms). An approximation scheme is developed for fold templates with C > 4, when threading requires too much memory and time to be practical on a typical workstation.