International Journal of Hybrid Intelligent Systems

Case-based reasoning is a popular approach used in intelligent systems. It is particularly useful in domains where an abundant number of past cases is available. Cases encompass knowledge accumulated from specific (specialized) situations. Whenever a new case has to be dealt with, the most similar cases are retrieved from the case base and their encompassed knowledge is exploited in the current situation. Combinations of case-based reasoning with other intelligent methods have been explored deriving effective knowledge representation schemes. Although some types of combinations have been mostly explored, other types have not been thoroughly investigated. In this paper, we briefly outline popular case-based reasoning combinations. More specifically, we focus on combinations of case-based reasoning with rule-based reasoning, soft computing methods (i.e., fuzzy methods, neural networks, genetic algorithms) and ontologies. We illustrate basic types of such combinations and also point out future directions.

Individual and/or hybrid AI techniques are often used in learning environments for well-structured domains to perform learner diagnosis, create and update a learner model and provide support at individual or group level. This paper presents a conceptual model that employs a synergistic approach based on Case-Based Reasoning (CBR) and Multicriteria Decision Making (MDM) components for learner modelling and feedback generation during exploration in an ill-defined domain of mathematical generalisation. The CBR component is used to diagnose what students are doing on the basis of simple and composite cases; simple cases represent parts of the models that the learners could possibly construct during an exploratory learning activity, while composite cases, which are assembled from simple cases, correspond to strategies that learners may adopt to construct their models. Similarity measures are used to identify how close/far are the learners from solutions pre-specified and stored in the knowledge base. This information is then fed into the MDM component that is responsible for prioritising types of feedback depending on the context. The operation of the two components and the effectiveness of the synergistic approach are validated through user scenarios in the context of an exploratory learning environment for mathematical generalisation.

In the current contribution, an application for constructing mutual fund portfolios is presented. This approach comprises several Intelligent Methods, namely an argumentation based decision making framework and a hybrid evolutionary forecasting algorithm which combines Genetic Algorithms (GA), MultiModel Partitioning (MMP) theory and Extended Kalman Filters (EKF). Specifically, the argumentation framework is employed in order to develop mutual funds performance models and select a small set of mutual funds, which will compose the final portfolio. On the other hand, the hybrid evolutionary forecasting algorithm is applied in order to forecast the market status (inflating or deflating) for the next investment period. The knowledge engineering approach and application development steps are also presented and discussed.

This study examines a hybrid Artificial Intelligence modelling approach in terms of its classification efficiency in the abdominal pain disease, especially in childhood. The classification model consists of a series of efficient Artificial Neural Network (ANN) architectures which have been evaluated by the application of an innovative Fuzzy Algebraic Information System (FAIS) [10]. FAIS offers a flexible approach by employing fuzzy sets and relations, fuzzy intensification and dilution techniques towards the assessment of neural models under different levels of accuracy and under various perspectives. The fact that FAIS produces an overall ANN evaluation index and also individual partial evaluation indices corresponding to each separate output neuron, makes it very useful for the specific disease where even the slightest error can cause the unnecessary operative treatment of the disease. In the examined cases, the produced ANN models have proven to perform classification with success. The whole approach comprises of a mixture of ANN development techniques together with Fuzzy modelling functions and relations and thus it can be considered as a Hybrid one.