Explicit information-seeking actions are needed to evaluate alternative actions in problem-solving tasks. Information-seeking costs are often traded off against the utility of information. We present three experiments that show how subjects adapt to the cost and information structures of environments in a map-navigation task. We found that subjects often stabilize at suboptimal levels of perfor...

In knowledge discovery and data mining many measures of interestingness have been proposed in order to measure the relevance and utility of the discovered patterns. Among these measures, an important role is played by Bayesian confirmation measures, which express in what degree a premise confirms a conclusion. In this paper, we are considering knowledge patterns in a form of “if..., then...” ru...

This paper argues that software testing can be less thorough yet more efficient if applied in a well-managed, empirical manner across the entire Software Development Life Cycle (SDLC). To ensure success, testing must be planned and executed within an Earned Value Management (EVM) paradigm. A specific example of empirical software testing is given: the Empirical Bayesian Stopping Rule (EBSR). Th...

Rule learning has the major advantage of understandability by human experts when performing knowledge discovery within the biomedical domain. Many rule learning algorithms require discrete data in order to learn the IF-THEN rule sets. This requirement makes the selection of a discretization technique an important step in rule learning. We compare the performance of one standard technique, Fayya...

Probabilistic models based on Bayes' rule are an increasingly popular approach to understanding human cognition. Bayesian models allow immense representational latitude and complexity. Because they use normative Bayesian mathematics to process those representations, they define optimal performance on a given task. This article focuses on key mechanisms of Bayesian information processing, and pr...

When observing data x1, . . . , xt modelled by a probabilistic distribution pθ(x), the maximum likelihood (ML) estimator θML = argmaxθ ∑︀t i=1 ln pθ(xi) cannot, in general, safely be used to predict xt+1. For instance, for a Bernoulli process, if only “tails” have been observed so far, the probability of “heads” is estimated to 0. Laplace’s famous “add-one” rule of succession (e.g., [Grü07]) re...

This paper is a comparison of how first-order Kyburgian Evidential Probability (EP), second-order EP, and objective Bayesian epistemology compare as to the KLM system-P rules for consequence relations and the monotonic / non-monotonic divide.

In recent years, there have been more and more enterprises using Web sites for marketing of various products or services; Internet thus allows customers shopping or searching for information online at any time and any location. If it is possible to recommend products to customers’ liking at the time they are visiting the specific web site, it would reduce the hassle customers experience in sear...