This paper presents a unifying framework to model casebased reasoning recommender systems (CBR-RSs). CBR-RSs have complex architectures and specialize the CBR problem solving methodology in a number of ways. The goal of the proposed framework is to illustrate both the common features of the various CBR-RSs as well as the points were these systems take different solutions. The proposed framework...

Case-Based Reasoning (CBR) can support hydraulic circuit design. Existing expert systems for hydraulic system design use production rules as its source of knowledge. However, this leads to problems of knowledge acquisition and knowledge base maintenance. This paper describes the application of CBR to hydraulic circuit design for production machines, which helps solving problems using past exper...

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the tr...

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the tr...

This paper brings together factor-based models of case-based reasoning (CBR) and the logical specification classifiers. Horty [8] has developed precedent into a theory precedential constraint. In this we combine binary-input classifier logic (BCL) to classifiers their explanations given by Liu & Lorini [13, 14] with Horty’s account CBR, since both CBR map sets features decisions or classifi...

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the tr...

Case-Based Reasoning (CBR) is one of most successful applied AI technologies of recent years. Although many CBR systems reason locally on a previous experience base to solve new problems, in this paper we focus on distributed retrieval processes working on a network of collaborating CBR systems. In such systems, each node in a network of CBR agents collaborates, arguments and counterarguments i...

This work presents the evaluation of two mission specification and task allocation architectures. These architectures, described in part 1 of this paper, present novel means with which to integrate a case-based reasoning (CBR) mission planner with contract net protocol (CNP) based task allocation. In the first design, the CBR and runtime-CNP architecture, the case-based mission planner generate...

The United Kingdom (UK) Ministry of Defence’s (MOD) Defence Science and Technology Laboratory (Dstl) have sponsored two key development activities relating to the OneSAF Computer Generated Forces (CGF) system and development of a Common Chemical, Biological and Radiological (CBR) modelling capability. The first is an interface to enable OneSAF to operate in a High Level Architecture (HLA) 1516-...

Development and usage of Case Based Reasoning (CBR) driven medical diagnostic system requires a large volume of clinical cases that depict the problem-solving methodology of medical experts. Successful usage of CBR based systems in healthcare is constrained by the need for a continuous supply of current and correct clinical cases (in an electronic medium) from medical experts. To address this c...