Case-based reasoning is the technique of solving new problems by adapting solutions that were used to solve old problems. This reliance on previous experiences (or cases) is a hallmark of case-based reasoning. Each case can contain a great deal of information including a description of the situation that was encountered, ways in which the situation di ered from similar situations, and how the s...

We have characterized the immunobiology of the interaction of intercellular adhesion molecule 3 (ICAM-3; CD50) with its counter-receptor, leukocyte function-associated antigen 1 (LFA-1; CD11a/CD18). Purified ICAM-3 supported LFA-1-dependent adhesion in a temperature- and cation-dependent manner. Activation of cells bearing LFA-1 increased adhesiveness for ICAM-3 in parallel to adhesiveness for ...

In theories and models of computational intelligence, cognition and action have historically been investigated on separate grounds. We conjecture that the main mechanism of case-based reasoning (CBR) applies to cognitive tasks at various levels and of various granularity, and hence can represent a bridge or a continuum between the higher and lower levels of cognition. CBR is an artificial intel...

Rifamycins have proven efficacy in the treatment of persistent bacterial infections. However, the frequency with which bacteria develop resistance to rifamycin agents restricts their clinical use to antibiotic combination regimens. In a program directed toward the synthesis of rifamycins with a lower propensity to elicit resistance development, a series of compounds were prepared that covalentl...

Case-Based Reasoning (CBR) is a recognised and well established method for building medical systems. In this paper, we identify strengths and weaknesses of CBR in medicine. System properties, divided into construction-oriented and purpose-oriented, are used as the basis for a survey of recent publications and research projects. The survey is used to find current trends in present medical CBR re...

Case-based reasoning (CBR) aims at using experience from the past in order to guide future problem solving rather than “starting from scratch” every time. We propose a CBR strategy particularly suitable for realizing this principle if heuristic search is used as a problem solving method: Given a new problem, a CBR method exploits previously solved problems in order to predict a region of the se...

When we use Case-Based Reasoning (CBR) for practical applications, it is often the case to implement two kinds of problem solvers: casebased one and knowledge-based/conventional one. The complex integration of such plural reasoners often causes serious VV~T problems: performance of problem solving, quality of solutions, and so on. In this paper, we ~ldress the performance validation problems of...

A CBR system needs a good case retention strategy to decide which cases to incorporate into the case base in order to maximize the performance of the system. In this work we present a collaborative case retention strategy, designed for multiagent CBR systems, called the Collaborative Case Bargaining strategy. The CCB strategy is a bargaining mechanism in which each CBR agent tries to maximize t...

customers and customer-service personnel. He argued that CBR systems are easy to assess, develop, and deploy (for example, QUICKSOURCE was created in six months) and can be evaluated in terms of their cost benefits. Finally, John Lemmer (Rome Laboratories) outlined lessons learned through his experience in managing the funding for five groups researching CBR for crisis battle planning. He descr...

By design, Case-Based Reasoning (CBR) systems do not need deep general knowledge. In contrast to (rule-based) expert systems, CBR systems can already be used with just some initial knowledge. Further knowledge can then be added manually or learned over time. CBR systems are not addressing a special group of users. Expert systems, on the other hand, are intended to solve problems similar to huma...