Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Case-Based Reasoning System and ANNs <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-Based Reasoning System and ANNs <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
design   , and some relating examples in engineering applications have been reported  BIB001  BIB002 . In this survey paper, the-state-of-art of connectionist model-based CBR system design is discussed. Special emphasis is laid on how the ANN can advance CBR technology by building an ANN-based CBR system, or integrating itself as a component within a CBR system. A comprehensively comparative study on the main feature of each method is presented. Furthermore, some other network models that are potentially useful in developing CBR systems are described. The rest of this paper is organised as follows. Section 2 presents some basic concepts concerning CBR. The nature of CBR and several key components involved in the reasoning process of CBR are introduced. Also, some problems and challenges met in a CBR system design are described. The importance of using an ANN technique in a CBR system is then discussed. In Section 3, the CBR is further discussed from the instance-based learning point of view, which provides an interesting bridge between CBR and ANN, as well as a basis for comparing and contrasting these two methods. Several ANN models proposed for constructing a CBR system and for solving some specific issues involved in the CBR process are summarised in Section 4. The main characteristics of each model are analysed, and the advantages and limitations of different models compared. On this basis, future research directions and other network models are suggested and discussed in Section 5. Finally, concluding remarks are given in Section 6. when a new instance is observed, and this then needs to be classified. Case-based reasoning is also an important approach of instance-based learning. In contrast to the relatively simple k-nearest neighbour algorithm and locally-weighted linear regression, CBR methods may use more general and rich symbolic descriptions to represent instances or cases. Also, more complex similarity metrics, such as the syntactic similarity measures, may be employed, and the target function to be 'approximated' may be a functional and logical relationship, or description, rather than the discretevalued or real-valued analytical functions. Therefore, CBR is a more complicated instance-based learning process. Its accomplishment may be more knowledge-intensive and rely on an effective combination of statistical, knowledge-based reasoning and other learning approaches. In the ANN research area, several types of network models exist, which are closely related to instance-based learning methods, and therefore may be used as a heuristic example to link and compare CBR to ANN approaches. The Radial Basis Function network   , for example, is a typical ANN learning paradigm which can synthesise local messages (locally-weighted distances) to construct a description to a given target function. Usually, the regression function expressed by RBF may be described as where w is the regression coefficient vector, ͖ is a set of kernel functions, and {x i ͖ is a set of example instances or training patterns chosen from the entire set of sample patterns. k is an integral constant that specifies the number of kernel functions to be included. Typically, the kernel function is specified as an n-dimensional Gaussian function centred at the point x i with some variance 2 i . This may be shown as measures similarity: the larger the radial distance between x and x i , the smaller the The topology of the RBF network is depicted in Fig. 1 . The network is a two-layer feedforward network with k hidden nodes. The nonlinear transfer function of each hidden node is described by a kernel function. The network output is obtained by a linear combination of the outputs of all hidden nodes with {w i ͖ as the parameters. Let X denote the entire set of N sample patterns. Each element x i in X is used as a training pattern and centralises a kernel function. Using the same criterion as Eq. (3), the optimal set of regression coefficient {w i ͖ is to be found to minimises the error