Case-based approaches predict the behaviour of dynamic systems by analysing a given experimental setting in the context of others. To select similar cases and to control adaptation of cases, they employ general knowledge. If that is neither available nor inductively derivable, the knowledge implicit in cases can be utilized for a case-based ranking and adaptation of similar cases. We introduce the system OASES and its application to medical experimental studies to demonstrate this approach. Keywords: Case-Based Reasoning; Case-Based Similarity Ranking; Case-Based Adaptation; Prediction; Experimental Studies 1 Introduction Simulation is an experiment performed on a model that is aimed at answering questions about the behaviour of a system. The experiment we are interested in is analysing and predicting the effect of settings in experimental medical studies. The subjects of these experimental studies define the system, namely the organisms that the model is intended to represent. Mode...

We present a Personalized Health Information Generation and Delivery System that leverages case based reasoning techniques to dynamically author a Personalized Health Information Package based on an individual's current health profile. The work features a compositional adaptation approach, whereby relevant health information elements from the solution component of multiple similar past cases are carefully selected and systematically combined to yield a new personalized health information package. We have implemented a generic Java-based case based reasoning engine that applies a novel compositional adaptation algorithm to author a HTML-based personalized health information package that can be emailed to users.

Abstract. Case-based reasoning (CBR) is a recent approach for many applications in medicine, particularly in decision support. In this paper we describe the CBR system FM-Ultranet, used in the domain of ultrasonography. Due to the hierarchical structure of the model and the definition of a similarity measure, we achieve an excellent retrieval of similar cases from the case base. Medical experts have evaluated FM-Ultranet in daily practice and the system has met their expectations. 1

Abstract. This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and Reinforcement Learning (RL) techniques. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging technique, the Heuristic Accelerated Reinforcement Learning (HARL), in which RL methods are accelerated by making use of heuristic information. CB-HARL is a subset of RL that makes use of a heuristic function derived from a case base, in a Case Based Reasoning manner. An algorithm that incorporates CBR techniques into the Heuristically Accelerated Q–Learning is also proposed. Empirical evaluations were conducted in a simulator for the RoboCup Four-Legged Soccer Competition, and results obtained shows that using CB-HARL, the agents learn faster than using either RL or HARL methods. 1

Abstract — Efficient representation of family tree is essential for any system utilizing information in the family tree. Efficient representation of family tree and retrieval methods, which can handle large number of family trees, are introduced in this paper. It is a modified form of conceptual graph and can be used as a case representation form for a case-based genetic cancer risk assessment system.

May we grow ever nearer as the years go by.