Case Based Reasoning and Context-Awareness

Context-sensitive processing has a key role in many modern intelligent IT applications, with context-awareness and context-reasoning being essential not only for mobile, pervasive, and ubiquitous computing, but also for a wide range of other areas such as recommender systems, collaborative software, web engineering, information sharing, health care workflow and patient control, adaptive games, autonomic systems, and e-Learning solutions. Context awareness in case-based reasoning (CBR) systems has also become a topic of increased research. In CBR, context serves as a major source for reasoning, decision-making, and adaptation. Consequently, achieving desired behaviors from CBR systems in these areas will depend on the ability to represent and manipulate information about a rich range of contextual factors. These factors may include not only physical characteristics of the task environment, but many other aspects such as the knowledge states (of both the application and user), and user beliefs and emotions. The representation and reasoning problem therein presents research challenges to which numerous methods and techniques derived from artificial intelligence and knowledge management (e.g., logical reasoning, object relationship models, ontologies, similarity measures, and intelligent retrieval mechanisms) are now being brought to bear. This workshop aims to bring together researchers and practitioners exploring issues and approaches for context-sensitive systems involving CBR to share their problems and techniques. It will examine mechanisms and techniques for structured storage of contextual information, effective ways to retrieve, reuse, and adapt it, as well as methods for enabling integration of context and application knowledge.

Case Based Reasoning in the Health Sciences

This workshop is the fifth in a series of exciting workshops held at previous ECCBR and ICCBR conferences. It focuses on the applications of case-based reasoning to the health sciences, and proposes to provide a forum for identifying and discussing important contributions and opportunities for research in this area. A special issue of Computational Intelligence journal will feature the best papers submitted to the workshop.

Textual Case-Based Reasoning: Beyond Retrieval

Textual CBR (TCBR) applies the CBR problem-solving methodology to situations where experiences are predominantly captured in text form. The aim of this workshop is to provide a forum for the discussion of trends, research issues and practical experience in TCBR. We are especially interested in issues that go beyond retrieval, such as solution adaptation, explanation, case base maintenance, and other issues. We have made this the workshop 'theme'. But, in order to encourage discussion of these issues, alongside the invitation to submit 'conventional' research and application papers, we also invite papers that address a common problem, which we refer to as the workshop challenge. The challenge that we propose consists in analysing the corpus of Air Investigation Reports available from the Transportation Safety Board of Canada. We are asking people to imagine that they are to use this corpus to build a TCBR system that supports human investigators. We hope that the combination of a `theme' and the challenge of analysing a common problem will lead to a lively, enjoyable and informative workshop!

Uncertainty and Fuzziness in Case-Based Reasoning

As a general problem solving methodology intended to cover a wide range of real-world applications, CBR must face the challenge to deal with uncertain, incomplete, and vague information. Correspondingly, recent years have witnessed an increased interest in formalizing parts of the CBR methodology within different frameworks of reasoning under uncertainty and, moreover, in building hybrid approaches by combining CBR with methods of uncertain and approximate reasoning, such as probability or fuzzy set theory. The objective of the workshop is to provide an opportunity for exchanging ideas related to the application of uncertainty techniques in CBR, for discussing advances in this field as well as open problems for future research.

Knowledge Discovery and Similarity

Case-based reasoning systems rely on a variety of techniques, such as data mining, machine learning, and knowledge discovery in order to build, maintain, and use their knowledge resources both for domain and system processing. In addition, these techniques rely on metrics for determining various kinds of similarity between aspects of domain and system knowledge. This workshop will bring together researchers and practitioners to explore issues and approaches for discovering, building, maintaining, and applying the essential underlying knowledge to support case-based reasoning systems. The workshop aims to provide an interdisciplinary forum for the exchange of new ideas and the discussion of future research directions.