Research on analogical mapping has not yet focused on the fate of information about the base and target domains that is not relevant to the correspondence. We suggest that there are two methods for dealing with irrelevant information in analogies. Nonalignable objects are ignored, while irrelevant

The generation of too much information prohibits efficient resolution proof search in classical logics. Hence subsumption is used to discard redundant information and strategies have been developed to guide the proof search avoiding irrelevant information. The extension of the resolution method

on the mechanisms of construction of the dynamic content of the WM. In JUDGEMAP2, the mechanism responsible for building the content of the WM (e.g. context) is a spreading activation mechanism. It leads to an unusual prediction – that irrelevant dimensions takes part in judgment. The data, obtained

In this paper, we consider one aspect of the problem of applying decision theory to the design of agents that learn how to make decisions under uncertainty. This aspect concerns how an agent can estimate probabilities for the possible states of the world, given that it only makes limited observations before committing to a decision. We show that the naive application of statistical tools can be improved upon if the agent can determine which of his observations are truly relevant to the estimation problem at hand. We give a framework in which such determinations can be made, and define an estimation procedure to use them. Our framework also suggests several extensions, which show how additional knowledge can be used to improve the estimation procedure still further.

We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features

, matched, composed and monitored for service selection and composition. However, very few of them have considered the problem of evaluating and certifying the quality of the provided service information to reduce irrelevant information for service consumers, which is crucial to improve the efficiency

This paper presents an unusual prediction made by the DUAL-based model of judgment JUDGEMAP and its verification. The model is shortly presented as well as the simulation data obtained with it. These data predict that people will use the information on an irrelevant dimension when judging another

The paper presents an experiment that tests the influence of an irrelevant to the task feature (namely the color of the fonts used) on the judgment of rent prices of various apartments. The rents were presented with either green or red digits that stand for the price of the apartments. Participants

by the consideration of irrele-vant information. Irrelevant perceptual input can be ignored by selecting between modalities. We may look or listen (Broadbent & Gregory, 1961). Recent experiments have concerned selection between receptors in the same modality, particularly the ears (Broadbent, 1954; Treisman, 1961

Even experienced users of IR systems experience a high degree of frustration in searching for information on the World Wide Web, in part because current search engines concentrate on speed and coverage at the expense of precision. In this paper, we describe an approach to increase precision