Case-based reasoning system

There are two types of knowledge-based systems, expert systems and case-based reasoning systems, which have been widely applied in various fields, such as fashion matching recommendation, software engineering, computer vision, computer-aided design and production management. We will introduce the most popular knowledge-based system, the rule-based expert system, in Section 2.2. 

Third, human experts oan reason by analogy. Even if the expert is faced with a problem for which there is no exact similar experience, the expert is still capable of coming up with approximate or probable solutions that need to be tried out for correctness. By reasoning from analogy with past cases Case-Based Reasoning systems can address new and unknown problems, and are not constrained to solving only a limited set of known problems. 

Case-Based Reasoning. Case-Based Reasoning (CBR) systems base their solutions on previously solved problems (cases) which are stored in a case-base [Watson Marir, 1994]. When a new problem is presented to a CBR system a similar case(s) is/are retrieved from the case-base. Depending on the differences between the retrieved and the presented problem the retrieved solution may have to be more or less adapted to obtain a solution to the new problem. The solved problem may be retained in the case base if deemed useful. 

Case-based reasoning. The main advantage of CBR systems for NDT data interpretation is that they can cope with data coming from inspection of varying constructions under varying conditions with various system settings due to their ability to learn from the data classified by the operator. In such situations no reliahle statistical classifier can be designed, and the rule-hased classifiers would be either very inefficient or unpractically complex. 

Eor a number of cognitive or interpretive tasks, there are alternatives to mainstream knowledge-based systems that may be more appropriate, especially if adaptive behavior and learning capabihty are important to system performance. Two approaches that embody these characteristics are neural networks (nets) and case-based reasoning. 

P. Koton, "SMARTPLAN A Case-Based Resource Allocation and Scheduling System," in Proceedings of a Workshop on Case-Based Reasoning, Morgan Kaufman, San Mateo, Calif., 1989. 

Case-based reasoning is very much dependent on the structure and content of its cases and their representation because case retrieval involves identifying those features in the problem that best match those in the case base. The dynamic addition of new cases means that CBR is intrinsically a learning methodology such that the performance of an expert system based on this approach will improve with time [9]. Systems may be developed with conventional computer languages or shells [7]. 

Aamodt A, Plaza E. Case-based reasoning—foundational issues, methodological variations and system approaches. AI Commun 1994 7 39-59. 

Watson I. Applying case-based reasoning techniques for enterprising systems. San Francisco Morgan Kaufmann, 1997. 

The fitness function was based on the inherent safety index, which was simplified It was noticed that there are only minor differences in the safety properties of the compounds in the process. Therefore most subindices are the same for all configurations. The equipment type used in all the configurations is the same (i.e. distillation). Therefore the subindex of equipment safety is constant too. Also the safety of process structures is quite the same since the distillation systems used are rather similar in configuration. Therefore the subindex for process structure was not evaluated and case-based reasoning was not needed. 

Most AI methods used in science lie within one of three areas evolutionary methods, neural networks and related methods, and knowledge-based systems. Additional methods, such as automated reasoning, hybrid systems, fuzzy logic, and case-based reasoning, are also of scientific interest, but this review will focus on the methods that seem to offer the greatest near-term potential in science. 

The operators are immediately able to compare the different situations and assess which of the proposed solutions, or a completely different one, to choose best. While the search for, and the presentation of the situation alternatives can be seen as the retrieve step of case-based reasoning (CBR, see Subsect. 7.-5.2), the selection and apphcation of an appropriate solution corresponds to the reuse aspect. By adapting the recommended course of action and carrying it out, the operator then provides the revision step. An important aspect of the system is that the possible actions are neither directly applied onto the process, nor are there any restrictions of the operators about the possible interventions. Instead, the operators experience is supported, but not replaced, by the recommender functionality of the support system. 

Aamodt, A., Plaza, E. Case-based reasoning Foundational issues, methodological variations, and system approaches. Al Communications 7(1), 39-59 (1994)... 

Slots can represent variables of different types (e.g., integer, real, symbol, interval) or functions (e.g., procedure, link) used in basic algorithms. The majority of systems using case-based reasoning, which is described later in this chapter, work with frames. 

Case-based reasoning (CBR) is a concept for problem solving based on solutions for similar problems. The central module of a CBR system is the case memory that stored the previously solved problems in form of a problem description and a problem solution. One of the first comprehensive approaches for this method was described by Aamodt and Plaza in 1994 [16]. The process of case-based reasoning consists of four steps ... 

Case-Based Reasoning (CBR) is a problem-solving system that relies on stored representations of previously solved problems and their solutions. 

Aamodt, A. and Plaza, E., Case-Based Reasoning Foundational Issnes, Methodological Variations, and System Approaches, AI Communications, 7(1), 39, 1994. 

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