CaseBaseGenerator for Intelligent Systems1

2010

In the presented paper, we proposed a concept of software realization of a hybrid expert system using case based reasoning. The presented paper focuses on that part of case-based reasoning system which is smoothly integrated into a rule-based reasoning system, thus coordinating case-based reasoning techniques and rule-based reasoning techniques in a unified automated reasoning system, in which an automated processor may proceed by inferential reasoning on the facts about the problem and the cases by means of rule-based reasoning techniques or based on procedural directives supplied by a human programmer, and may select the case which is the best match for the given problem, but may act differently from the precise action prescribed for that case. The solution for dynamically adapting the case base to the problems; which the automated case based reasoning system encounters, in which the system may create additional cases which may exemplify the problem or which may be useful for future problems. The solution in which the system may be set to work with a limited case base, and may solicit human advice for treatment of new problems which are not already well-treated by the case base reasoning system.

Knowledge-Based Systems, 1996

A model to implement the case-based reasoning developed from using a neural net as a base to calculate a measure of similarity between the new problem and each case is presented. It is shown how the neural net provides a mechanism to retrieve cases considering information that in other models needs a parallel architecture. Moreover, in this paper it is analyzed how the model can be employed to create expert networks and case-based planners. ~ 1997 Elsevier Science B.V. All rights reserved.

2009

Case-based reasoning (CBR) is a sub-field of Artificial Intelligence that deals with experience-based problem solving. CBR has its roots in different disciplines such as cognitive science, machine learning, and knowledge-based systems. Today, it is a well established research field of its own, which produced a rich variety of specific methods, as well as applications implementing those methods for particular tasks and domains. This paper gives a compact overview of CBR in general and further discusses recent advancements in selected topics.

International Journal of Hybrid Intelligent Systems, 2009

Case-based reasoning is a popular approach used in intelligent systems. Whenever a new case has to be dealt with, the most similar cases are retrieved from the case base and their encompassed knowledge is exploited in the current situation. Combinations of case-based reasoning with other intelligent methods have been explored deriving effective knowledge representation schemes. Although some types of combinations have been mostly explored, other types have not been thoroughly investigated. In this paper, we briefly outline popular case-based reasoning combinations. More specifically, we focus on combinations of case-based reasoning with rulebased reasoning, soft computing and ontologies. We illustrate basic types of such combinations and discuss future directions.

Expert Systems With Applications, 2001

The success of a case-based reasoning (CBR) system largely depends on an effective maintenance of its case-base. This study proposes a genetic algorithms (GAs) approach to the maintenance of CBR systems. This approach automatically determines the representation of cases and indexes relevant attributes to grasp the rapidly changing environment around the system. In this study, the proposed model is applied to stock market analysis. Experimental results show that the proposed model outperforms conventional CBR systems. q

2002

In this paper we present CAT-CBR a component-based platform for developing CBR systems. CAT-CBR uses UPML (Universal Problem-solving Methods Language) for specifying CBR components. A collection of CBR components for retrieval of propositional cases is presented in detail. The CAT-CBR platform guides the engineer using a case-based recommendations system to develop a configuration of components that satisfies the requirements of a CBR system application. We also present how to develop a runtime CBR application from the configuration resultant of the configuring process.

Ai Communications, 1994

Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case-based reasoning, describes some of the leading methodological approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summarized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

1997

This paper gives a survey about existing case-based reasoning systems and what can be learned from their development and usage. The information gathering for this survey has been carried out by sending two structured questionnaires to developers of case-based reasoning systems. On the basis of 50 answers coming from all over the world we classify and summarise the results concerning the cases acquired and used, the tasks fulfilled, the background knowledge needed and interesting experiences from bringing the systems into use. The information gathered enables us to identify those steps in the CBR cycle [AaPl94] that require further research and development. Finally we give an outlook to the next step currently underway that is directed towards making the collected CBR cases available in a CBR system that is accessible via WWW. By means of this 'meta CBR system' we give the CBR community an opportunity to learn from experience through using the CBR technology for the improvement of our own field.

The Knowledge Engineering Review, 2005

AI communications, 1997

This paper contains a brief overview of case-based reasoning (CBR) with an emphasis on European activities in the field. The main objective was to have a balance between brevity and expressiveness and providing helpful pointers to the field. It identifies major open problems of CBR associated with: retrieval/selection, memory organization, matching, adaptation/evaluation, forgetting and, finally, integration with other techniques. It is intended for readers with knowledge in the area and contains a list of almost one hundred references in the field.