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.
40999406
Multi-agent collaboration for B2B workflow monitoring
Business-to-business (B2B) applications environments are exceedingly dynamic and competitive. This dynamism is manifested in the form of changing process requirements and time constraints. However, current workflow management technologies have difficulties to solve the challenges problems, such as: how to deal with the dynamic nature of B2B commerce processes, how to manage the distributed knowledge and recourses, and how to reduce the transaction risk. In this paper, a collaborative multi-agent system has been proposed. Multiple intelligent agents in our system can work together not only to identify the workflow problems, but also to solve such problems, by applying business rules, such as re-organizing the procurement and the transaction processes, and making necessary workflow process changes.
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.
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.
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.
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.
9795964
On finding disjoint paths in single and dual link cost networks
Finding a disjoint path pair is an important component in survivable networks. Since the traffic is carried on the active (working) path most of the time, it is useful to find a disjoint path pair such that the length of the shorter path (to be used as the active path) is minimized. In this paper, we first address such a minmin problem. We prove that this problem is NP-complete in either single link cost (e.g. dedicated backup bandwidth) or dual link cost (e.g. shared backup bandwidth) networks. In addition, it is NP-hard to obtain a k-approximation to the optimal solution for any k>1. Our proof is extended to another open question regarding the computational complexity of a restricted version of the min-sum problem in an undirected network with ordered dual cost links (called MSOD problem). To solve the minmin problem efficiently, we introduce a novel concept called conflicting link set which provides insights into the so-called trap problem, and develop a divide-and-conquer strategy. The result is an effective heuristic for the minmin problem called COLE, which can outperform other approaches in terms of both the optimality and running time. We also apply COLE to the MSOD problem to efficiently provide shared path protection and conduct comprehensive performance evaluation as well as comparison of various schemes for shared path protection. We show that COLE not only processes connection requests much faster than existing ILP based approaches but also achieves a good balance among the AP length, bandwidth efficiency and recovery time.
19364030
The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees
Abstract Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modeled using the evidential reasoning (ER) approach. Several types of uncertainties such as ignorance and fuzziness can be modeled in the ER framework. In this paper, the ER approach will be extended to model new types of uncertainties including interval belief degrees and interval data that could be incurred in decision situations such as group decision making. The Dempster–Shafer (D–S) theory of evidence is first extended, which is one of the bases of the ER approach. The analytical ER algorithm is used to combine all evidence simultaneously. Two pairs of nonlinear optimization models are constructed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities of each alternative, respectively. Interval data are equivalently transformed to interval belief degrees and are incorporated into the nonlinear optimization models. A cargo ship selection problem is examined to show the implementation process of the proposed approach.
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.
18364869
Threat-driven modeling and verification of secure software using aspect-oriented Petri nets
Design-level vulnerabilities are a major source of security risks in software. To improve trustworthiness of software design, this paper presents a formal threat-driven approach, which explores explicit behaviors of security threats as the mediator between security goals and applications of security features. Security threats are potential attacks, i.e., misuses and anomalies that violate the security goals of systems' intended functions. Security threats suggest what, where, and how security features for threat mitigation should be applied. To specify the intended functions, security threats, and threat mitigations of a security design as a whole, we exploit aspect-oriented Petri nets as a unified formalism. Intended functions and security threats are modeled by Petri nets, whereas threat mitigations are modeled by Petri net-based aspects due to the incremental and crosscutting nature of security features. The unified formalism facilitates verifying correctness of security threats against intended functions and verifying absence of security threats from integrated functions and threat mitigations. As a result, our approach can make software design provably secured from anticipated security threats and, thus, reduce significant design-level vulnerabilities. We demonstrate our approach through a systematic case study on the threat-driven modeling and verification of a real-world shopping cart application.
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.