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

We study the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be made in each, and the goal of the learner is to make few mistakes. We are interested in the case that the learner has reason to believe that one of some pool

estimates for power-law data, based on maximum likelihood methods and the Kolmogorov-Smirnov statistic. We also show how to tell whether the data follow a power-law distribution at all, defining quantitative measures that indicate when the power law is a reasonable fit to the data and when it is not. We

in the management literature, in fact little is known about the reasons why "enriched" work sometimes leads to positive outcomes for workers and for their employing organizations. Even less is known about the relative effectiveness of various strategies for carrying out the redesign of work One reason

nothing directly to do with coding or decoding will show that in some sense belief propagation "converges with high probability to a near-optimum value" of the desired belief on a class of loopy DAGs Progress in the analysis of loopy belief propagation has been made for the case of networks

presumably extensive and dense model of general domain knowledge as explanatory support for case-based problem solving and learning. A generic reasoning method - captured in what is called the ACTIVATE-EXPLAIN-FOCUS cycle - is able to utilise a rich knowledge model in producing contextdependent

. The framework provides a formal model of argumentation-based reasoning and negotiation, details a design philosophy which ensures a clear link between the formal model and its practical instantiation, and describes a case study of this relationship for a particular class of architectures (namely those

-oriented newsgroups according to subject, achieving as high as an 80 % correct classification rate. There are also several obvious directions for improving the system’s classification performance in those cases where it did not do as well. The system is based on calculating and comparing profiles of N

Conversational case-based reasoning (CCBR) was the first widespread commercially successful form of case-based reasoning. Historically, commercial CCBR tools conducted constrained human-user dialogues and targeted customer support tasks. Due to their simple implementation of CBR technology

data, and determine which data to use in the process of solving a problem or participating in some kind of realistic analysis or investigation. Research in case-based reasoning (CBR), which provides a plausible model of learning from problem solving situations, makes suggestions about education