A considerable amount of research in case-based reasoning (CBR) has recently focused on conversational CBR as a means of providing more effective support for interactive problem solving. We review progress made to date and identify challenges that remain to be addressed.

This commentary provides a definition of textual case-based reasoning (TCBR) and surveys research contributions according to four research questions. We also describe how TCBR can be distinguished from text mining and information retrieval. We conclude with potential directions for TCBR research.

This paper is the synthesis of joint work realised in a technical forum group within the AgentLink III NoE framework, which elaborated on issues concerning self-organization and emergence in multi-agent systems (MAS). The work concluded on a common definition of the concepts of self-organization and emergence in MAS and the associated properties and characteristics. Also it developed towards an approach for selecting self-organization mechanisms using a number of selected reference case studies and a set of evaluation criteria.

Distribution of resources within case-based reasoning (CBR) architectures is beneficial in a variety of application contexts. This article briefly discusses some of the approaches that fall under the heading of distributed CBR, and their general impact.

This paper presents an overview on the role of agents in the Semantic Web, which was the topic of the AgentLink Technical Forum Group on ‘Semantic Web Agents’, aimed at fostering closer collaboration between the European communities working in these areas. The paper is structured in three main sections. In the first, we argue how agents are an essential component of the Semantic Web, then we provide a brief history, by no means comprehensive, of how the Semantic Web vision—that includes agents—has evolved over the past fifteen years. We then conclude, reporting on the topics presented and discussed during the Technical Forum.

The aim of this commentary is to discuss the contribution of soft computing—a consortium of fuzzy logic, neural network theory, evolutionary computing, and probabilistic reasoning—to the development of case-based reasoning (CBR) systems. We will describe how soft computing has been used in case representation, retrieval, adaptation, reuse, and case-base maintenance, and then present a brief summary of six CBR applications that use soft computing techniques.

The design task is especially appropriate for applying, integrating, exploring and pushing the boundaries of case-based reasoning. In this paper, we briefly review the challenges that design poses for case-based reasoning and survey research on case-based design ranging from early explorations to more recent work on innovative design. We also summarize the theoretical contributions this research has made to case-based reasoning itself.

Case-based reasoning (CBR) is widely applicable to the diagnosis of problems and the identification of solutions to them. This review of the literature identifies key papers relating to this use of CBR. The stages in diagnosing and troubleshooting a problem are considered and the elements of a CBR system for achieving this are described. Five systems are discussed that have frequently been cited in the literature and illustrate the use of CBR in diagnosis and troubleshooting applications.

We briefly examine case-based planning starting with the seminal work of Hammond. Derivational analogy represents an important shift of technical emphasis that helped mature the techniques. The choice of abstraction level is equally important. We conclude by discussing theoretical underpinnings and by providing some pointers to current directions.

This commentary summarizes case-based reasoning research applied in the medical domain. In this commentary the term ‘medical’ is used in an all-encompassing manner. It comprises all aspects of health, for example, from diagnosis to nutrition planning. This article provides references to researchers in the field, systems, workshops, and landmark publications.

A primary research stream that contributed to the birth of case-based reasoning (CBR) was Artificial Intelligence and Law. Since law is largely about cases, it is a particularly interesting domain for CBR researchers. This article surveys some of the historically significant systems and developments in this field.

This commentary briefly reviews work on the application of case-based reasoning (CBR) to the design and construction of educational approaches and computer-based teaching systems. The CBR cognitive model is at the core of constructivist learning approaches such as Goal-based Scenarios and Learning by Design. Case libraries can play roles as intelligent resources while learning and frameworks for articulating one's understanding. More recently, CBR techniques have been applied to design and construction of simulation-based learning systems and serious games. The main ideas of CBR are explained and pointers to relevant references are provided, both for finished work and on-going research.

This commentary describes two core knowledge management approaches that applied case-based reasoning as a methodological foundation for organizational systems managing experience. These research projects illustrate the presence of knowledge management in case-based reasoning by focusing on the dualism between case-based reasoning and organizational approaches targeting knowledge management goals.

This commentary summarizes case-based reasoning (CBR) research applied to image processing. It includes references to the systems, workshops, and landmark publications. Image processing includes a variety of image formats, from computer tomography images to remote sensing and spatial data sets.

We describe recommender systems and especially case-based recommender systems. We define a framework in which these systems can be understood. The framework contrasts collaborative with case-based, reactive with proactive, single-shot with conversational, and asking with proposing. Within this framework, we review a selection of papers from the case-based recommender systems literature, covering the development of these systems over the last ten years.

This commentary describes notable commercial applications of case-based reasoning, including systems that have been in continuous profitable use for over a decade. It is divided into sections on engineering applications, helpdesk applications and on-line case-based reasoning.

The basic principle underpinning case-based reasoning (CBR) is that new problems can be solved by reusing solutions to past problems. The generality of this idea means that CBR is finding application in a wide variety of areas. The special advantage of CBR is that a case can be a very convenient means of capturing knowledge, especially in weak theory domains where the relationship between causes and effects may not be well understood. Cases may embody more than problem-solving knowledge; the knowledge in a case may be a creative structure or a complex behavior pattern. The widespread applicability of this idea means that it has been exploited in a diverse range of areas across the arts and sciences. This article provides a brief summary of some of these applications.