Dongming Xu

An improved algorithm for the packing of unequal circles within a larger containing circle

Abstract This paper describes an approved algorithm for the problems of unequal circle packing – the quasi-physical quasi-human algorithm. First, the quasi-physical approach for the general packing problems is described in solving the pure problems of unequal circle packing. The method is an analogy to the physical model in which a number of smooth cylinders are packed inside a container. A quasi-human strategy is then proposed to trigger a jump for a stuck object in order to get out of local minima. Our method has been tested in numerical experiments. The computational results are presented, showing the merits of the proposed method. Our algorithm can be thought as an adoptive algorithm of the Tabu search.

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.

An intelligent agent-assisted decision support system for family financial planning

The demand for family financial planning (FFP) services is growing dramatically as the financial market grows more complex and people become more aware of the importance of qualified financial guidance. To provide decision support for FFP-related decisions, we formulate a conceptual model for FFP by following Simons decision-making process model and map our model to the generic FFP process. The design, development, and empirical investigation of an intelligent FFP system for supporting FFP decisions by utilizing intelligent agents and Web-services technology are presented.

Conceptual modeling and development of an intelligent agent-assisted decision support system for anti-money laundering

Criminal elements in todays technology-driven society are using every means available at their disposal to launder the proceeds from their illegal activities. In response, international anti-money laundering (AML) efforts are being made. The events of September 11, 2001, highlighted the need for more sophisticated AML and anti-terrorist financing programs across the industry and nation. In the wake of this, regulators are focusing on the role that technology can play in compliance with laws and ultimately in law enforcement. Banks will have to employ or enhance AML tools and technology to satisfy rising regulatory expectations. While many AML solutions have been in place for some time within the banks, they are faced with the challenge of adapting to the ever-changing risks and methods related to money laundering. In order to provide support for AML decisions, we have formulated an AML conceptual model by following Simon [Simon, H. A. (1977). The new science of management decision. Englewood Cliffs, NJ: Prentice-Hall] decision-making process model. Based on this model, a novel and open multi-agent AML system prototype has been designed and developed. Intelligent agents with their properties of autonomy, reactivity, and proactivity are well suited for dynamic, ill-structured, and complex ML prevention controls. The advanced architecture is able to provide more adaptive, intelligent, and flexible solution for AML. This paper is the first attempt at intelligent agent financial application in the AML domain, with a decision-making/problem-solving process model, an innovative agent-based architecture, and a prototype system.

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.

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.

Enhancing e-learning effectiveness using an intelligent agent-supported personalized virtual learning environment: an empirical investigation

Virtual learning environments (VLEs) developed under constructivism and embedded personalization learning functions have the potential to meet different requirements of different learners and thus increase e-Learning effectiveness. We formulated internal personalized learning mechanisms by implementing intelligent agents in a VLE under a constructivist learning model and further developed an e-learning effectiveness framework by integrating educational and IS theories. An empirical field experiment involving 228 university students was conducted. The findings suggested that personalized e-learning facilities enhance online learning effectiveness in terms of examination, satisfaction, and self-efficacy criteria.

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.

Conflict detection and resolution for workflows constrained by resources and non-determined durations

The correctness of a workflow specification is critical for the automation of business processes. Therefore, errors in the specification should be detected and corrected at build-time. In this paper, we present a conflict verification and resolution approach for a kind of workflow constrained by resources and non-determined duration based on Petri net. In this kind of workflow, there are two timing functions for each activity to present the minimum and maximum duration of each activity, and the implementations of some activities require resources. Based on the Petri net model obtained, the earliest time to start each activity can be calculated and the key activities influencing the implementation of the workflow can be determined, with which the resource consistency between activities can be verified. Key-activity and waiting-short priority strategies are adopted to remove the resource conflicts between activities, which can ensure that most of the subsequent activities start as early as possible and that the whole workflow be finished in a shorter time. Through experiments, it is proved that the proposed removal strategy for resource conflicts is better than other strategies.

Outbound logistics exception monitoring: A multi-perspective ontologies’ approach with intelligent agents

Logistics consists of a complex network of organizations and processes where exception monitoring is critical for the success of logistics service. In order to detect exceptions effectively, exception monitoring requires proper understanding of the possible exceptions. However, the extant exception monitoring approaches or systems still lack sufficient emphasis in exceptions understanding. This paper presents a novel outbound logistics exception monitoring approach by incorporating multi-perspective ontologies and intelligent agents. Specially, the multi-perspective ontologies, involving static ontology, social ontology and dynamic ontology, are firstly employed to develop the taxonomy of the logistics exception, to reflect the situation dependencies of logistics exception and to represent the dynamic nature of business processes. From this point forwards, an outbound exception monitoring system is designed by introducing multi-intelligent agents, which can ensure autonomous, flexible, and collaborative exception monitor in logistics service. Finally, the presented approach and designed system are exhibited through a case study of two ubiquitous logistics exceptions, which indicates that the proposed multi-perspective ontologies provide better understanding of exceptions thereby enabling the designed outbound exception monitoring system to perform well.