Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Introduction <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> BIB001 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Introduction <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> BIB002 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Introduction <s> Chapter 1: What Is Case-Based Reasoning? Chapter 2: Understanding CBR Chapter 3: The Application of CBR Chapter 4: Industrial Applications of CBR Chapter 5: CBR and Customer Service Chapter 6: CBR Software Tools Chapter 7: Building a Diagnostic Case-Base Chapter 8: Building, Testing, and Maintaining Case-Bases Chapter 9: Conclusion Chapter 10: Bibliography <s> BIB003 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Introduction <s> We present a process model that applies case-based reasoning techniques to information system design. Describing the process model we focus on similarity and case adaption aspects. We are currently implementing the process model in a research prototype called CBModeler. <s> BIB004 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Introduction <s> This paper presents the CARE-PARTNER system. Functionally, it offers via the WWW knowledge-support assistance to clinicians responsible for the long-term follow-up of stem-cell post-transplant patient care. CARE-PARTNER aims at implementing the concept of evidence-based medical practice, which recommends the practice of medicine based on proven and validated knowledge. From an artificial intelligence viewpoint, it proposes a multimodal reasoning framework for the cooperation of case-based reasoning, rule-based reasoning and information retrieval to solve problems. The role of case-based reasoning is presented in this paper as the collection of evidence for evidence-based medical practice. Case-based reasoning permits to refine and complete the knowledge of the system. It enhances the system by conferring an ability to learn from experience, and thus improve results over time. <s> BIB005 </s> Case-Based Reasoning System and Artificial Neural Networks: A Review <s> Introduction <s> In the recent years we have developed three medical Case-Based Reasoning systems for diagnosis of dysmorphic syndromes, for antibiotics therapy advice and for course analysis and prognosis of the kidney function. In this paper we summarise our experience with these medical CBR applications. Special focuses are on the retrieval methods and on the importance and the design of typical cases (prototypes) which we use as a knowledge generalisation step to fill the gap between single cases and general knowledge. <s> BIB006
As a significant branch of Artificial Intelligence (AI), Case-Based Reasoning (CBR) has received more and more research attention. In the last few decades, CBR has grown from a quite new area to a subject of major influence. Much work has been dedicated to this topic, including its basic principle BIB001  , methodologies  BIB002 and applications BIB003 . CBR systems have also been used to solve a wide range of problems. Examples of the applications of the Correspondence and offprint requests to: D. Chen, KnowledgeBased Systems Centre, School of Computing, Information Systems and Mathematics, South Bank University, 103 Borough Road, London SE1 OAA, UK. E-mail: chendȰsbu.ac.uk CBR systems include medical diagnosis BIB005 BIB006 , time series prediction  , product design  BIB004 and planning   , etc. The basic idea behind CBR is that reasoning and problem-solving are based on the most specific experiences available instead of general abstract knowledge or rules. In this way, casebased reasoning provides a new method for building intelligent systems. However, although CBR is simple in principle, and has been successfully used in engineering problem-solving, it still lacks a generally theoretically sound framework. A systematically mathematical model has not been established for CBR system design, analysis, comparison and test. Consequently, most CBR systems could not complete their reasoning process, and propose a solution to a given task without intervention of domain experts or system managers BIB003 . Many CBR systems, for example, actually act as case retrieval and proposal systems BIB002 , while case adaptation and case update are performed by human experts. On the other hand, based on specific application backgrounds of interest, different CBR systems usually use different approaches for case representation, case-base construction, case indexing, and criteria for case matching and adaptation: some are quantitative and algorithm-based; and the others are descriptive, constructive and experience-based. As such, although all the CBR systems share the same basic principle in common, it may be difficult to compare and contrast one CBR system with another, so that the performances of different CBR systems can be estimated and verified on a common benchmark. Recently, several methodologies have been put forward for using the connectionist approach (i.e. Artificial Neural Networks (ANN)) in CBR system 265 