Qiubang Li

Unified problem modeling language for knowledge engineering of complex systems-part II

The 90’s has seen the emergence of hybrid configurations of four most commonly used intelligent methodologies, namely, symbolic knowledge based systems (e.g. expert systems), artificial neural networks, fuzzy systems and genetic algorithms. These hybrid configurations are used for different problem solving tasks/situations. In this paper we describe unified problem modeling language at two different levels, the task structure level for knowledge engineering of complex data intensive domains, and the computational level of the task level hybrid architecture. Among other aspects, the unified problem modeling language considers various intelligent methodologies and their hybrid configurations as technological primitives used to accomplish various tasks defined at the task structure level. The unified problem modeling language is defined in the form of five problem solving adapters. The problem solving adapters outline the goals, tasks, percepts/inputs, and hard and soft computing methods for modeling complex problems. The task structure level has been applied in modeling several applications in e-commerce, image processing, diagnosis, and other complex, time critical, and data intensive domains. We also define a layered intelligent multi-agent, operating system processes, intelligent technologies with the task structure level associative hybrid architecture. The layered architecture also facilitates component based software modeling process.

Multi-layered multi-agent architecture with fuzzy application in electrical power systems

Intelligent agents (or software agents) today can be seen as providing support to practitioners and problem solvers at three levels, namely, clerical, tool and task levels. In particular, we outline computational level based agent definitions of fuzzy neural agents. We show the application of these agents in determining faulty components in a electrical power system network.

Performance optimization of data mining applications using a multi-layered multi-agent data mining architecture

This paper proposes a human-centered multi-layered multi-agent architecture for performance optimization of data mining applications. Feedback from user is used for optimizing the data mining performance. This paper describes application of the architecture in banking and finance domain

An adaptive E-commerce personalization framework with application in E-banking

Internet Personalized services are irresistible developing trend for e-commerce. More and more researchers are committed to personalization field. Many personalization approaches are static and lack of means to improve the personalized tasks. This paper proposes an adaptive e-commerce personalization framework using traditional data mining techniques and agent technology as well as user feedback optimisation mechanism to improve the personalized services to the e-commerce customer. The behaviours of all the agents in the framework are carefully considered and the framework has been applied to an online banking system.

Patterns of Behaviour Mediated by Cognitive Scripts and Emotional Attitudes - Context-Aware Engineering of Data Mining Systems

In this paper we outline a seven layer context-aware data mining architecture which combines context, sensemaking (cognitive and affective) and data mining technologies to design adaptive context-aware data mining systems. We particularly show how cognitive constructs and emotional attitudes of a user mediate in interpretation of meaning in hidden patterns. We illustrate the role of cognitive constructs in interpreting a CRM situation by a relationship manager in a banking and finance application. We also illustrate the role of emotional attitudes as an important factor in context-aware interpretation of mined behavioral patterns in a sales recruitment and benchmarking application

Dynamic Customer Profiling Architecture Using High Performance Computing

Identifying profiles of customers based on their history trails of transactions and further providing personalization services to them are the best strategy to retain customers for online businesses. Currently profiling techniques have the defects of static offline operation, inexactness, and slow response, which do not meet the ever-changing demand of online transactions for e-commerce, e-banking, etc. This paper proposes a dynamic intelligent profiling architecture with the help of high performance computing. The architecture is tested and applied in an e-banking application.