The paper describes the first phase of development of a Case-Base Reasoning (CBR) system to support early conceptual design of buildings. As specific context of application, the research focuses on energy performance of commercial buildings, and the early identification of energy-related features that contribute to its outcomes. The hypothesis is that bringing knowledge from relevant precedents may facilitate this identification process, thus offering a significant contribution for early analysis and decision-making. The paper introduces a proof-of-concept for such a system, proposing a novel integration of Case-Based Reasoning, Parametric Modeling (Building Information Modeling), and Ontology Classification. Potential advantages and limitations of this three-level integration approach are discussed along with recommendations for future development.

This paper describes PATSy, an online repository of virtual patient cases for training and research for >students and practitioners in the clinical sciences. A typical student session with PATSy is illustrated. An extension to PATSy that adds vicarious learning resources (VL-PATSy) is also described. The concept of vicarious learning is outlined and results from a study of learning outcomes from VL-PATSy are presented. PATSy and VL-PATSy will be demonstrated at the symposium.

Case Based Reasoning and particularly Estimation by Analogy, has been used in a number of problem-solving areas, such as cost estimation. Conventional methods, despite the lack of a sound criterion for choosing nearest projects, were based on estimation using a fixed and predetermined number of neighbors from the entire set of historical instances. This approach puts boundaries to the estimation ability of such algorithms, for they do not take into consideration that every project under estimation is unique and requires different handling. The notion of distributions of distances together with a distance metric for distributions help us to adapt the proposed method (we call it DD-EbA) each time to a specific case that is to be estimated without loosing in prediction power or computational cost. The results of this paper show that the proposed technique achieves the above idea in a very efficient way.

The first major story generation system, which preceded Minstrel and which While ongoing progress in digital entertainment also received significant attention, is Tale-Spin (Meehan technology continues, commercial designers still largely 1977). Like Minstrel, this system generates stories which eschew systems for procedural story generation, preferring satisfy user-submitted requirements. Tale-Spin creates instead to generate content by hand. In the academic English stories by planning a method for the main literature, projects such as (Appling & Riedl 2009, Roberts character to achieve her or his goal, using inferences and & Isbell 2009) continue to investigate ways to improve the rules to generate a large number of details about a story nuances of interactive storytelling while others attempt to (many of which do little contribute to an audience create their own systems to investigate ways to use experience). This contrasts nicely with Minstrel, which knowledge from interactive narrative and story generation performs no logical inferences and which performs all in new fields such as playable games (Drachen & Hitchens actions from the point of view of an author, manipulating et al. 2009, Sullivan, Mateas & Wardrip-Fruin 2009).

The work presented in this paper is part of a multidisciplinary team collaborating in the deployment of an autonomous oceanographic probe with the task of exploring marine regions and take phytoplankton samples for their subsequent analysis in a laboratory. We will describe an autonomous system that, from sensor data, is able to characterize phytoplankton structures. Because the system has to work inboard, a main goal of our approach is to dramatically reduce the dimensionality of the problem. Specifically, our development uses two AI techniques, namely Particle Swarm Optimization and Case-Based Reasoning. We report results of experiments performed with simulated environments.

An Analysis of Current Trends in CBR Research Using Multi-View Clustering

The European Conference on Case-Based Reasoning (CBR) in 2008 marked 15 years of international and European CBR conferences where almost seven hundred research papers were published. In this report we review the research themes covered in these papers and identify the topics that are active at the moment. The main mechanism for this analysis is a clustering of the research papers based on both co-citation links and text similarity. It is interesting to note that the core set of papers has attracted citations from almost three thousand papers outside the conference collection so it is clear that the CBR conferences are a sub-part of a much larger whole. It is remarkable that the research themes revealed by this analysis do not map directly to the sub-topics of CBR that might appear in a textbook. Instead they reflect the applications-oriented focus of CBR research, and cover the promising application areas and research challenges that are faced.

Adaptation has long been considered as the Achilles' heel of case-based reasoning since it requires some domain-specific knowledge that is difficult to acquire. In this paper, two strategies are combined in order to reduce the knowledge engineering cost induced by the adaptation knowledge (AK) acquisition task: AK is learned from the case base by the means of knowledge discovery techniques, and the AK acquisition sessions are opportunistically triggered, i.e., at problem-solving time.

In case-based reasoning, the adaptation of a source case in order to solve the target problem is at the same time crucial and difficult to implement. The reason for this difficulty is that, in general, adaptation strongly depends on domain-dependent knowledge. This fact motivates research on adaptation knowledge acquisition (AKA). This paper presents an approach to AKA based on the principles and techniques of knowledge discovery from databases and data-mining. It is implemented in CABAMAKA, a system that explores the variations within the case base to elicit adaptation knowledge. This system has been successfully tested in an application of case-based reasoning to decision support in the domain of breast cancer treatment.

This text analyses the papers accepted for the workshop "Reuse of designs: an interdisciplinary cognitive approach". Several dimensions and questions considered as important (by the authors and/or by us) are addressed: What about the "interdisciplinary cognitive" character of the approaches adopted by the authors? Is design indeed a domain where the use of CBR is particularly suitable? Are there important distinctions between CBR and other approaches? Which types of knowledge -other than cases- is being, or might be, used in CBR systems? With respect to cases: are there different "types" of case and different types of case use? which formats are adopted for their representation? do cases have "components"? how are cases organised in the case memory? Concerning their retrieval: which types of index are used? on which types of relation is retrieval based? how does one retrieve only a selected number of cases, i.e., how does one retrieve only the "best" cases? which processes and strategies are used, by the system and by its user? Finally, some important aspects of CBR system development are shortly discussed: should CBR systems be assistance or autonomous systems? how can case knowledge be "acquired"? what about the empirical evaluation of CBR systems? The conclusion points out some lacking points: not much attention is paid to the user, and few papers have indeed adopted an interdisciplinary cognitive approach.