Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Case Indexing and Case Retrieval <s> We propose a probabilistic case-space metric for the case matching and case adaptation tasks. Central to our approach is a probability propagation algorithm adopted from Bayesian reasoning systems, which allows our case-based reasoning system to perform theoretically sound probabilistic reasoning. The same probability propagation mechanism actually offers a uniform solution to both the case matching and case adaptation problems. We also show how the algorithm can be implemented as a connectionist network, where efficient massively parallel case retrieval is an inherent property of the system. We argue that using this kind of an approach, the difficult problem of case indexing can be completely avoided. <s> BIB001 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Case Indexing and Case Retrieval <s> In this paper we present a case-based approach we have used for the realization of HUMOS, a hybrid user modeling shell system that has been designed for building and maintaining long term models of individual Internet users. A distinguished feature of HUMOS is the hybrid approach to user modeling, where a sub-symbolic module is integrated into a case-based reasoner. A case study in information filtering systems on the World Wide Web is also presented. The described system, named WIFS, is based on HUMOS and is capable of selecting HTML/text documents, collected from the Web, according to the interests and characteristics of the user. Presently the system acts as an intelligent interface for the Web search engines. The experimental results we have obtained are encouraging and support the choice of the case-based approach to user modeling for adaptive systems. <s> BIB002 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Case Indexing and Case Retrieval <s> The early stages of design are characterised by imprecise and incomplete information about the features and properties of targeted product or process. Equipment selection is one of the decisions to be taken in an initial phase of design. Mixing is an example of process where the selection of the equipment is practically realised ‘by hand’. An application of rule-based or object-oriented tools in the selection of mixing equipment encounters many problems. The main reasons are the lack of well-structured knowledge and the great diversity of mixing problems. Case-based reasoning constitutes a good tool to handle the problems of mixing equipment selection. An instance generator for case-based reasoning system is described in this work. A fuzzy neural network system has been used to produce the cases. The following types of the equipment for heat exchange in stirred vessels have been considered: plain jacket, plain jacket with agitation nozzles, plain jacket with spiral baffles, dimple jacket, halfpipe jacket, helical coil, vertical coil and vertical plate coil. <s> BIB003
Case indexing involves assigning a case's indexes that describe its features and distinguish it from other cases. Case indexing is important for case matching, retrieving and updating. An effective case indexing can facilitate case retrieval and case update in a case base. In a connectionist framework, a case is usually described by a vector with features or attributes as its components. Therefore, several known approaches for case indexing and retrieval are mainly based on, to a certain extent, investigating and utilising the relationship between cases and features. As a result, case indexing and case retrieval can be integrated into a single network. Perhaps the simplest method for case indexing and retrieval is to code or store a case as the connecting weights between two adjacent layers in a feedforward layered network, coupled with a specific choice of a node transfer function. Each node in the lower and higher layers represents one attribute and one case, respectively. The linked weight from each attribute node to a case node is set to the corresponding component of the case that the case node is associated with, whilst the function of each case node is specified by a simple linear function. When a new case is presented to the lower layer, a dot product of this new case vector and each old case, expressed by the weight vector, is calculated, respectively. The result is then shown as the activity level of the corresponding case node, and indicates the similarity between the new and old cases. Consequently, a set of cases with higher activity levels is retrieved. An example of this method can be found in Myllymaki and Tirri BIB001 . The main strength of this approach lies in its computational simplicity, and that it can naturally be integrated into the parallel structure of the network. On the other hand, however, it is not practical to store all the cases at hand into a network, especially when the cases are corrupted with noise. Therefore, an optimal strategy should be addressed for the proper choice of cases. A more general and complex approach for case indexing and retrieval is to construct a mapping from the attribute domain (feature space) to the case space, based on a set of chosen attribute-case sample patterns. Various methods and network structures have been used for this purpose. In Becker and Jazayeri  , knowledge about the relation between features and cases, and the dependencies among the features, are inducted to a set of rules. These rules are then transformed into a set of connecting weights between feature and case nodes, as well as a strategy to control the activity of case nodes, based on the activity of feature nodes. Thus, a non-analytical mapping from feature to case is established. The multilayer feedforward neural network was introduced for case indexing and retrieval   BIB002 . Because of its powerful non-linear mapping ability, the MFNN is particularly well suited for describing complicated feature-case relationships. To fulfil desired input-output requirements, the network is trained with a set of feature-case pairs. In particular the network can perform a multi-level case retrieval, which is more useful when features, sub-classes and general cases are structured in hierarchial levels. As shown in Main and Dillon  , a number of MFNNs are used to describe a hierarchy of interwoven levels of cases, where the lowest level (i.e. the most fundamental level) corresponds to a set of features, and the highest level represents a set of general cases. The relationship between each two levels is normally non-linear. Beginning from the lowest level, one network is trained to perform a mapping from the chosen features to a set of sub-classes. A further network is then trained to map these subclasses to a set of sub-classes at the following higher level. This is repeated upward for each two levels until the highest level classes are reached. When a current case with a set of features is input to the trained hierarchial network, output at each level of the network can be used to aid in retrieving of a set of cases or sub-classes. In addition, a feedforward three-layer fuzzy neural network has been suggested for case retrieval BIB003 , where the transfer functions of hidden and output nodes are specified as logical operators, such as logical OR and AND operations. The connecting weights between each two adjacent layers represent the influence of nodes in the lower layer on the output of nodes in the upper layer. Essentially, such a method views the case retrieving as a multiple criteria decision making process.