A multi-objective evolutionary algorithm fitness function for case-base maintenance.

Case-Base Maintenance (CBM) has two important goals. On the one hand, it aims to reduce the size of the case-base. On the other hand, it has to improve the accuracy of the CBR system. CBM can be represented as a multi-objective optimization problem to achieve both goals. Multi-Objective Evolutionary Algorithms (MOEAs) have been recognised as appropriate techniques for multi-objective optimisation because they perform a search for multiple solutions in parallel. In the present paper we introduce a fitness function based on the Complexity Profiling model to perform CBM with MOEA, and we compare its results against other known CBM approaches. From the experimental results, CBM with MOEA shows regularly good results in many case-bases, despite the amount of redundant and noisy cases, and with a significant potential for improvement

How Case-Based Reasoning on e-Community Knowledge Can Be Improved Thanks to Knowledge Reliability

International audienceThis paper shows that performing case-based reasoning (CBR) on knowledge coming from an e-community is improved by taking into account knowledge reliability. MKM (meta-knowledge model) is a model for managing reliability of the knowledge units that are used in the reasoning process. For this, MKM uses meta-knowledge such as belief, trust and reputation, about knowledge units and users. MKM is used both to select relevant knowledge to conduct the reasoning process, and to rank results provided by the CBR engine according to the knowledge reliability. An experiment in which users perform a blind evaluation of results provided by two systems (with and without taking into account reliability, i.e. with and without MKM) shows that users are more satisfied with results provided by the system implementing MKM

The Science of Sungrazers, Sunskirters, and Other Near-Sun Comets

This review addresses our current understanding of comets that venture close to the Sun, and are hence exposed to much more extreme conditions than comets that are typically studied from Earth. The extreme solar heating and plasma environments that these objects encounter change many aspects of their behaviour, thus yielding valuable information on both the comets themselves that complements other data we have on primitive solar system bodies, as well as on the near-solar environment which they traverse. We propose clear definitions for these comets: We use the term near-Sun comets to encompass all objects that pass sunward of the perihelion distance of planet Mercury (0.307 AU). Sunskirters are defined as objects that pass within 33 solar radii of the Sun’s centre, equal to half of Mercury’s perihelion distance, and the commonly-used phrase sungrazers to be objects that reach perihelion within 3.45 solar radii, i.e. the fluid Roche limit. Finally, comets with orbits that intersect the solar photosphere are termed sundivers. We summarize past studies of these objects, as well as the instruments and facilities used to study them, including space-based platforms that have led to a recent revolution in the quantity and quality of relevant observations. Relevant comet populations are described, including the Kreutz, Marsden, Kracht, and Meyer groups, near-Sun asteroids, and a brief discussion of their origins. The importance of light curves and the clues they provide on cometary composition are emphasized, together with what information has been gleaned about nucleus parameters, including the sizes and masses of objects and their families, and their tensile strengths. The physical processes occurring at these objects are considered in some detail, including the disruption of nuclei, sublimation, and ionisation, and we consider the mass, momentum, and energy loss of comets in the corona and those that venture to lower altitudes. The different components of comae and tails are described, including dust, neutral and ionised gases, their chemical reactions, and their contributions to the near-Sun environment. Comet-solar wind interactions are discussed, including the use of comets as probes of solar wind and coronal conditions in their vicinities. We address the relevance of work on comets near the Sun to similar objects orbiting other stars, and conclude with a discussion of future directions for the field and the planned ground- and space-based facilities that will allow us to address those science topics

The Origin, Early Evolution and Predictability of Solar Eruptions

Coronal mass ejections (CMEs) were discovered in the early 1970s when space-borne coronagraphs revealed that eruptions of plasma are ejected from the Sun. Today, it is known that the Sun produces eruptive flares, filament eruptions, coronal mass ejections and failed eruptions; all thought to be due to a release of energy stored in the coronal magnetic field during its drastic reconfiguration. This review discusses the observations and physical mechanisms behind this eruptive activity, with a view to making an assessment of the current capability of forecasting these events for space weather risk and impact mitigation. Whilst a wealth of observations exist, and detailed models have been developed, there still exists a need to draw these approaches together. In particular more realistic models are encouraged in order to asses the full range of complexity of the solar atmosphere and the criteria for which an eruption is formed. From the observational side, a more detailed understanding of the role of photospheric flows and reconnection is needed in order to identify the evolutionary path that ultimately means a magnetic structure will erupt

Case-Based Reasoning: Experiences, Lessons, and Future Directions

Case-based reasoning (CBR) is now a mature subfield of artificial intelligence. The fundamental principles of case-based reasoning have been established, and numerous applications have demonstrated its role as a useful technology. Recent progress has also revealed new opportunities and challenges for the field. This book presents experiences in CBR that illustrate the state of the art, the lessons learned from those experiences, and directions for the future. True to the spirit of CBR, this book examines the field in a primarily case-based way. Its chapters provide concrete examples of how key issues---including indexing and retrieval, case adaptation, evaluation, and application of CBR methods---are being addressed in the context of a range of tasks and domains. These issue-oriented case studies of experiences with particular projects provide a view of the principles of CBR, what CBR can do, how to attack problems with case-based reasoning, and how new challenges are being addressed. The case studies are supplemented with commentaries from leaders in the field providing individual perspectives on the state of CBR and its future impact. This book provides experienced CBR practitioners with a reference to recent progress in case-based reasoning research and applications. It also provides an introduction to CBR methods and the state of the art for students, AI researchers in other areas, and developers starting to build case-based reasoning systems. It presents experts and non-experts alike with visions of the most promising directions for new progress and for the roles of the next generation of CBR systems.

Engineering and Learning of Adaptation Knowledge in Case-Based Reasoning

International audienceCase-based reasoning (CBR) uses various knowledge containers for problem solving: cases, domain, similarity, and adaptation knowledge. These various knowledge containers are characterised from the engineering and learning points of view. We focus on adaptation and similarity knowledge containers that are of first importance, difficult to acquire and to model at the design stage. These difficulties motivate the use of a learning process for refining these knowledge containers. We argue that in an adaptation guided retrieval approach, similarity andadaptation knowledge containers must be mixed. We rely on a formalisation of adaptation for highlighting several knowledge units to be learnt, i.e. dependencies and influences between problem and solution descriptors.Finally, we propose a learning scenario called active approach where the user plays a central role for achieving the learning steps

Provenance, trust and sharing in peer-to-peer case-based web search

9th European Conference, ECCBR 2008, Trier, Germany, September 1-4 2008Despite the success of modern Web search engines, challenges remain when it comes to providing people with access to the right information at the right time. In this paper, we describe how a novel combination of case-based reasoning, Web search, and peer-to-peer networking can be used to develop a platform for personalized Web search. This novel approach benefits from better result quality and improved robustness against search spam, while offering an increased level of privacy to the individual user.Science Foundation Irelan

Keep it Simple: A Case-Base Maintenance Policy Based on Clustering and Information Theory

Abstract. Today’s case based reasoning applications face several challenges. In a typical application, the case bases grow at a very fast rate and their contents become increasingly diverse, making it necessary to partition a large case base into several smaller ones. Their users are overloaded with vast amounts of information during the retrieval process. These problems call for the development of effective case-base maintenance methods. As a result, many researchers have been driven to design sophisticated case-base structures or maintenance methods. In contrast, we hold a different point of view: we maintain that the structure of a case base should be kept as simple as possible, and that the maintenance method should be as transparent as possible. In this paper we propose a case-base maintenance method that avoids building sophisticated structures around a case base or perform complex operations on a case base. Our method partitions cases into clusters where the cases in the same cluster are more similar than cases in other clusters. In addition to the content of textual cases, the clustering method we propose can also be based on values of attributes that may be attached to the cases. Clusters can be converted to new case bases, which are smaller in size and when stored distributedly, can entail simpler maintenance operations. The contents of the new case bases are more focused and easier to retrieve and update. To support retrieval in this distributed case-base network, we present a method that is based on a decision forest built with the attributes that are obtained through an innovative modification of the ID3 algorithm.