Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Case-Based Reasoning System <s> Artificial neural net models have been studied for many years in the hope of achieving human-like performance in the fields of speech and image recognition. These models are composed of many nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural nets. Computational elements or nodes are connected via weights that are typically adapted during use to improve performance. There has been a recent resurgence in the field of artificial neural nets caused by new net topologies and algorithms, analog VLSI implementation techniques, and the belief that massive parallelism is essential for high performance speech and image recognition. This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification. These nets are highly parallel building blocks that illustrate neural net components and design principles and can be used to construct more complex systems. In addition to describing these nets, a major emphasis is placed on exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components. Single-layer nets can implement algorithms required by Gaussian maximum-likelihood classifiers and optimum minimum-error classifiers for binary patterns corrupted by noise. More generally, the decision regions required by any classification algorithm can be generated in a straightforward manner by three-layer feed-forward nets. <s> BIB001 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Case-Based Reasoning System <s> Case-based reasoning, broadly construed, is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents is using case-based reasoning. It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving. Case-based reasoning (CBR) has been formalized as a four-step process:N 1. Retrieve: Given a target problem, retrieve cases from memory that are relevant to solving it. A case consists of a problem, its solution, and, typically, annotations about how the solution was derived. For example, suppose Fred wants to prepare blueberry pancakes. Being a novice cook, the most relevant experience he can recall is one in which he successfully made plain pancakes. The procedure he followed for making the plain pancakes, together with justifications for decisions made along the way, constitutes Fred's retrieved case. 2. Reuse: Map the solution from the previous case to the target problem. This may involve adapting the solution as needed to fit the new situation. In the pancake example, Fred must adapt his retrieved solution to include the addition of blueberries. 3. Revise: Having mapped the previous solution to the target situation, test the new solution in the real world (or a simulation) and, if necessary, revise. Suppose Fred adapted his pancake solution by adding blueberries to the batter. After mixing, he discovers that the batter has turned blue -- an undesired effect. This suggests the following revision: delay the addition of blueberries until after the batter has been ladled into the pan. 4. Retain: After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory. Fred, accordingly, records his newfound procedure for making blueberry pancakes, thereby enriching his set of stored experiences, and better preparing him for future pancake-making demands. At first glance, CBR may seem similar to the rule-induction algorithmsP of machine learning.N Like a rule-induction algorithm, CBR starts with a set of cases or training examples; it forms generalizations of these examples, albeit implicit ones, by identifying commonalities between a retrieved case and the target problem. For instance, when Fred mapped his procedure for plain pancakes to blueberry pancakes, he decided to use the same basic batter and frying method, thus implicitly generalizing the set of situations under which the batter and frying method can be used. The key difference, however, between the implicit generalization in CBR and the generalization in rule induction lies in when the generalization is made. A rule-induction algorithm draws its generalizations from a set of training examples before the target problem is even known; that is, it performs eager generalization. For instance, if a rule-induction algorithm were given recipes for plain pancakes, Dutch apple pancakes, and banana pancakes as its training examples, it would have to derive, at training time, a set of general rules for making all types of pancakes. It would not be until testing time that it would be given, say, the task of cooking blueberry pancakes. The difficulty for the rule-induction algorithm is in anticipating the different directions in which it should attempt to generalize its training examples. This is in contrast to CBR, which delays (implicit) generalization of its cases until testing time -- a strategy of lazy generalization. In the pancake example, CBR has already been given the target problem of cooking blueberry pancakes; thus it can generalize its cases exactly as needed to cover this situation. CBR therefore tends to be a good approach for rich, complex domains in which there are myriad ways to generalize a case. <s> BIB002 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Case-Based Reasoning System <s> From the Publisher: ::: This book presents a selection of recent progress, issues, and directions for the future of case-based reasoning. It includes chapters addressing fundamental issues and approaches in indexing and retrieval, situation assessment and similarity assessment, and in case adaptation. Those chapters provide a "case-based" view of key problems and solutions in context of the tasks for which they were developed. It also presents lessons learned about how to design CBR systems and how to apply them to real-world problems. The final chapters include a perspective on the state of the field and the most important directions for future impact. The case studies presented involve a broad sampling of tasks, such as design, education, legal reasoning, planning, decision support, problem-solving, and knowledge navigation. In addition, they experimentally examine one of the fundamental tenets of CBR, that reasoning from prior experiences improves performance. The chapters also address other issues that, while not restricted to CBR per se, have been vigorously attacked by the CBR community, including creative problem-solving, strategic memory search, and opportunistic retrieval. This volume provides a vision of the present, and a challenge for the future, of case-based reasoning research and applications. <s> BIB003
Classical knowledge-or rule-based decision support systems draw conclusions by applying generalised rules, step-by-step, starting from scratch. Although successful in many application areas, such systems have met several problems in knowledge acquisition and system implementation. Inspired by the role of reminding in human reasoning, CBR systems have been proposed as an alternative to rule-based systems, where knowledge or rule elicitation are a difficult or intractable process. In principle, the CBR approach takes a different view, in that reasoning and problem-solving are performed by applying the experience that a system has acquired. This approach focuses on how to exploit human experience, instead of rules, in problem-solving, and thus improving the performance of decision support systems. In brief, reasoning in CBR is based on experience or remembering. In CBR, a primary knowledge, stored in memory, is not compiled from rules, but a set of structured cases. These cases represent an experience or lesson that, under which situations and to what problems, some specific solutions have been derived in the past to achieve the goals of the reasoner, and the effects after the solutions have been applied. When a new problem is presented to the system, a new solution is proposed by retrieving the most relevant old cases and adapting them to fit new situations, which is then introduced as a new case into the system in order to enrich the case base. Therefore, a case-based reasoner solves new problems by adapting solutions that were used to solve old problems BIB002 , and with efficient adaptation strategies and a proper complement of new cases, a CBR reasoner is expected to work more and more effectively. Generally speaking, successful use of CBR depends upon addressing issues of how to acquire, represent, index, retrieve and adapt existing cases. However, the most important operations involved in a primary reasoning process of CBR are: case retrieval and case adaptation, together with case similarity assessment and case adaptation criteria. For a new problem or situation, a set of the most similar cases is retrieved by the CBR, based on similarity assessment criteria, and the solution, corresponding to these previous cases, is adapted to propose a solution for the given problem based on the adaptation criteria. It is obvious that the criteria for both case similarity assessment and case adaptation are inextricably linked, which means that the solution suggested is intimately connected to the similarities and differences between new and old cases. More precisely, such a statement may be described as where {C Old ͖ and C New denote a set of old cases and a new case, respectively. S(·,·) is the similarity assessment criterion, and A, an operator, is defined by the adaptation criteria. Therefore, in essence, the CBR reasoning process is a pattern matching and classification process. Developing case similarity assessment criteria and case adaptation criteria is a central open challenge for CBR. In a symbolic description model-based CBR system, the case adaptation process is often performed by a rule-based system, which makes the CBR system design re-confront the knowledge acquisition problem for rule-based systems, and consequently, contradicts the important motivation for using CBR in problem-solving. As a result, many CBR systems currently work primarily as a case retrieval and proposal system, leaving the case adaptation to be undertaken by field experts. Some new methods have been proposed to overcome the case adaptation problems  BIB003 . Basically, however, these approaches are not easy to handle without the help of human experts or system designers. On the other hand, for a quantitative description model-based CBR system, the system design may become more flexible compared with symbolic description model-based systems. This is because a lot of mathematical approaches and optimisation techniques are available for defining, synthesising and analysing the case similarity assessment and case adaptation criteria. In particular, the connectionist approach and several related ANN models have been suggested for the CBR system design. As is well known, ANN approaches have many appealing characteristics, such as parallelism, robustness, adaptability and generalisation. Neural networks have the potential to provide some human characteristics of problem-solving that are difficult to describe and analyse using the logical approach of expert systems. More importantly, learning and problem-solving can be incorporated naturally and effectively by using a network. In a pattern classification problem, for example, classifiers based on a Multilayer Feedforward Neural Network (MFNN)  BIB001 and a Radial Basis Function (RBF) Network   can perform not only the classification itself, but also internally extract input pattern features. For such networks, the feature extraction and classification can be considered as being merged in the single classifier network. In addition, some methods have been put forward to extract symbolic representations (i.e. rules) from a trained network  [23]   , which makes it possible to combine the network with the symbolic description model-based CBR system. In the following sections, the connectionist modelbased approaches for CBR system design are discussed in detail. As an introduction in the next section, a comparison between CBR and ANN is made from the machine learning viewpoint  .