One problem which frequently surfaces when applying explanation-based learning (EBL) to imperfect theories is the multiple inconsistent explanation problem. The multiple inconsistent explanation problem occurs when a domain theory produces multiple explanations for a training instance, only some

is aligned to a cumulative global model, and then merged to the model. Because different parts of the model are updated independently while there are errors in the registration, such an approachmay result in an inconsistent model. In this paper, we study the problem of consistent registration of multiple

. Traditional demand deposit contracts which provide liquidity have multiple equilibria, one of which is a bank run. Bank runs in the model cause real economic damage, rather than simply reflecting other problems. Contracts which can prevent runs are studied, and the analysis shows

in an inconsistent and often incorrect fashion. The problem we study here is the task of merging data from multiple sources in as efficient manner as possible, while maximizing the accuracy of the result. We call this the merge/purge problem. In this paper we detail the sorted neighborhood method that is used

. However, this leads to problems of identifying and handling inconsistencies between such perspectives. Maintaining absolute consistency is not always possible. Often this is not even desirable since this can unnecessarily constrain the development process, and can lead to the loss of important information

. These behavioral results have corresponding welfare implications: With salient costs, mild self-control problems can severely damage a person only if she is naive, while with salient rewards mild self-control problems can severely damage a person only if she is sophisticated. We also consider a multiple

A complete theory of object recognition is an impossibility not simply because of the multiplicity of visual cues we exploit in elegant coordination to identify an object, but primarily because recognition involves fixation of belief, and anything one knows may be relevant. We finesse this obstacle

-automatic ontology merging: iPrompt is an interactive ontologymerging tool that guides the user through the merging process, presenting him with suggestions for next steps and identifying inconsistencies and potential problems. AnchorPrompt uses a graph structure of ontologies to find correlation between concepts

Large-scale software development is an evolutionary process. In an evolving specification, multiple development participants often hold multiple, inconsistent views on the system being developed, and considerable effort is spent handling recurrent inconsistencies. Detecting and resolving

obtained in a single database context, such results are directly applicable to data integration, where multiple information sources may provide data that are inconsistent with respect to the global view of the sources. 1.