Zhaohao Sun

R 5 model for case-based reasoning

This paper reviews some existing models of case-based reasoning (CBR) such as the R4 model of CBR and proposes a R5 model, in which repartition, retrieve, reuse, revise and retain are the main tasks for the CBR process. The original idea behind this model is that case base building is an important part of CBR and the case base can be built based on partitioning of the possible world of problems and solutions. It argues that the proposed R5 model is a new approach to using similarity-based reasoning to unify case base building, case retrieval, and case adaptation, and therefore facilitates the development of CBR with applications.

Similarity and metrics in case‐based reasoning

Similarity is a core concept in case‐based reasoning (CBR), because case base building, case retrieval, and even case adaptation all use similarity or similarity‐based reasoning. However, there is some confusion using similarity, similarity measures, and similarity metrics in CBR, in particular in domain‐dependent CBR systems. This article attempts to resolve this confusion by providing a unified framework for similarity, similarity relations, similarity measures, and similarity metrics, and their relationship. This article also extends some of the well‐known results in the theory of relations to similarity metrics. It appears that such extension may be of significance in case base building and case retrieval in CBR, as well as in various applied areas in which similarity plays an important role in system behavior.

Experience management in knowledge management

This paper examines experience and knowledge, experience management and knowledge management, and their interrelationships. It also proposes process perspectives for both experience management and knowledge management, which integrate experience processing and corresponding management, knowledge processing and corresponding management respectively. The proposed approach will facilitate research and development of knowledge management and experience management as well as knowledge-based systems.

A waterfall model for knowledge management and experience management

This paper examines experience and knowledge, experience management (EM) and knowledge management (KM), and their interrelationships. It then proposes waterfall models for both EM and KM. The models characterize EM and KM as the integration of experience processing and corresponding management, that of knowledge processing and corresponding management respectively. The proposed approach facilitates research and development of KM, EM, and hybrid intelligent systems.

Case base building with similarity relations

This paper has two main contributions. Firstly, it shows that similarity relations are an adequate means of formalization not only for case retrieval but also for case base building. Secondly, this paper provides a theoretical formalization for building case bases in case-based reasoning and presents three algorithms for case base building. The proposed approach argues that case base building can be based on both similarity relations and fuzzy similarity relations, which are both defined on the possible world of problems and solutions respectively. Thus case base building is a form of similarity-based reasoning. This approach is an extension for the logical and fuzzy approach to case based reasoning. The proposed methods and algorithms can be applied to reduction of case base size.

Analytics Service Oriented Architecture for Enterprise Information Systems

Big data analytics and business analytics are disruptive technology and innovative solution for enterprise development. However, what is the relationship between big data analytics and business analytics? What is the relationship between business analytics and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? These are still big issues for EIS development. This paper addresses these three issues by proposing an ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA can enhance to develop EIS. This paper also discusses the interrelationship between data analysis and business analytics, and between data analytics and big data analytics. The proposed approaches in this paper will facilitate research and development of EIS, business analytics, big data analytics, and business intelligence.

Experience based reasoning for recognising fraud and deception

Fraud, deception and their recognition have received increasing attention in multiagent systems (MAS), e-commerce, and agent societies. However, little attention has been given to the theoretical foundation for fraud and deception from a logical viewpoint. We fill this gap by arguing that experience-based reasoning (EBR) is a logical foundation for recognizing fraud and deception. It provides a logical analysis of deception, which classifies recognition of deception into knowledge-based deception recognition, inference-based deception recognition, and hybrid deception recognition. It will examine the relationship between EBR and fraud as well as deception. It uses EBR to recognize fraud and deception in e-commerce and MAS. The proposed approach will facilitate research and development of recognition of fraud and deception in e-commerce.

A logical foundation for the case-based reasoning cycle

Case‐based reasoning (CBR) has drawn considerable attention in artificial intelligence (AI) fields with many successful applications in systems such as e‐commerce and multiagent systems. For the moment, research and development of CBR basically follows the traditional process model of CBR, i.e., the R4 model and problem space model introduced in 1994 and 1996, respectively. However, there has been no logical analysis for this popular CBR model. This article will fill this gap by providing a unified logical foundation for the CBR cycle. The proposed approach is based on an integration of traditional mathematical logic, fuzzy logic, and similarity‐based reasoning. At the same time, we examine the CBR cycle from the knowledge‐based (KB) viewpoint. The proposed logical approach can facilitate research and development of CBR.

Abductive case-based reasoning

This article introduces abductive case‐based reasoning (CBR) and attempts to show that abductive CBR and deductive CBR can be integrated in clinical process and problem solving. Then it provides a unified formalization for integration of abduction, abductive CBR, deduction, and deductive CBR. This article also investigates abductive case retrieval and deductive case retrieval using similarity relations, fuzzy similarity relations, and similarity metrics. The proposed approach demonstrates that the integration of deductive CBR and abductive CBR is of practical significance in problem solving such as system diagnosis and analysis, and will facilitate research of abductive CBR and deductive CBR.

Five Perspectives on Case Based Reasoning

This article examines case-based reasoning (CBR) from five dif ferent perspectives: cognitive, process-oriented, logical, intelligent, and hy brid perspective. These five perspectives cover the majority of CBR research and development and classify CBR into five categories consequently: Cog nitive CBR, process CBR, logical CBR, intelligent CBR and hybrid CBR. This article looks at each of these perspectives on CBR from a unified view point taking into account corresponding fundamentals, applications and re lated issues. The proposed approach can facilitate research and development of CBR and its applications.