Using an integrated process of data and modeling in HRA

The paper describes an approach taken to estimate the probabilities of failure associated with various railroad tasks to prevent accidents (principally collisions and derailments). These probabilities were estimated using an expert elicitation process that used partially relevant data available from a variety of databases and that were filtered and scaled to make them more directly relevant to the analyses being performed. Extensive qualitative studies were performed prior to the elicitation process to identify relevant contexts under which the tasks can be performed.     

The Emotional Dog Gets Mistaken for a Possum.

H. D. Saltzstein and T. Kasachkoff (2004) (see record 2004-21519-004) critique the social intuitionist model (J. Haidt, 2001) (see record 2001-18918-008), but the model they critique is a stripped-down version that should be called the "possum" model. They make 3 charges about the possum model that are not true about the social intuitionist model: that it includes no role for reasoning, that it reduces social influence to compliance, and that it does not take a developmental perspective. After a defense of the honor of the social intuitionist model, this article raises 2 areas of legitimate dispute: the scope and nature of moral reasoning and the usefulness of appealing to innate ideas, rather than to learning and reasoning, as the origin of moral knowledge. This article presents 3 clusters of innate moral intuitions, related to sympathy, hierarchy, and reciprocity. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Extrapolation of fuzzy values from incomplete data bases

This paper presents different approaches which enable a data base management system to obtain a plausible fuzzy estimate for an attribute value of an item for which the information is not explicitly stored in the data base. This can be made either by a kind of analogical reasoning from information about particular items or by means of expert rules which specify the (fuzzy) sets of possible values of the attribute under consideration, for various classes of items. Another kind of expert rules enables the system to compute an estimate from the attribute value of another item provided that, in other respects, this latter item sufficiently resembles the item, the value of which we are interested in; then these expert rules are used either for controlling the analogical reasoning process or for enlarging the scope of application of the first kind of expert rules. The different approaches are discussed in the framework of possibility theory.     

Markov analysis of qualitative dynamics1

Common sense sometimes predicts events to be likely or unlikely rather than merely possible. We extend methods of qualitative reasoning to predict the relative likelihoods of possible qualitative behaviors by viewing the dynamics of a system as a Markov chain over its transition graph. This involves adding qualitative or quantitative estimates of transition probabilities to each of the transitions and applying the standard theory of Markov chains to distinguish persistent states from transient states and to calculate recurrence times, settling times, and probabilities for ending up in each state. Much of the analysis depends solely on qualitative estimates of transition probabilities, which follow directly from theoretical considerations and which lead to qualitative predictions about entire classes of systems. Quantitative estimates for specific systems are derived empirically and lead to qualitative and quantitative conclusions, most of which are insensitive to small perturbations in the estimated transition probabilities. The algorithms are straightforward and efficient.     

An approach to efficient business intelligent system for financial prediction

A concept of business intelligent system for financial prediction is considered in this paper. It provides data needed for fast, precise and good business decision support to all levels of management. The aim of the project is the development of a new online analytical processing oriented on case-based reasoning (CBR) where a previous experience for every new problem is taken into account. Methodological aspects have been tested in practice as a part of the management information system development project of “Novi Sad Fair”. A case study of an improved application of CBR in prediction of future payments is discussed in the paper. This paper is originally presented at The International Conference on Hybrid Information Technology 2006, at the special session on “Intelligent Information Systems for Financial Engineering”, November 2006 in Cheju Island, Korea.     

A Refinement Calculus for Shared-Variable Parallel and Distributed Programming

Parallel computers have not yet had the expected impact on mainstream computing. Parallelism adds a level of complexity to the programming task that makes it very error-prone. Moreover, a large variety of very different parallel architectures exists. Porting an implementation from one machine to another may require substantial changes. This paper addresses some of these problems by developing a formal basis for the design of parallel programs in the form of a refinement calculus. The calculus allows the stepwise formal derivation of an abstract, low-level implementation from a trusted, high-level specification. The calculus thus helps structuring and documenting the development process. Portability is increased, because the introduction of a machine-dependent feature can be located in the refinement tree. Development efforts above this point in the tree are independent of that feature and are thus reusable. Moreover, the discovery of new, possibly more efficient solutions is facilitated. Last but not least, programs are correct by construction, which obviates the need for difficult debugging. Our programming/specification notation supports fair parallelism, shared-variable and message-passing concurrency, local variables and channels. The calculus rests on a compositional trace semantics that treats shared-variable and message-passing concurrency uniformly. The refinement relation combines a context-sensitive notion of trace inclusion and assumption-commitment reasoning to achieve compositionality. The calculus straddles both concurrency paradigms, that is, a shared-variable program can be refined into a distributed, message-passing program and vice versa. Received July 2001 / Accepted in revised form May 2002     

The problem of definition expansion and contraction

This article is the fourth of a series of articles discussing various open research problems in automated reasoning. The problem proposed for research focuses on finding criteria that would enable an automated reasoning program to expand or contract definitions wisely. For evaluating a proposed solution to this research problem, we include suggestions concerning possible test problems.This work was supported by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy, under contract W-31-109-Eng-38.     

Intelligent handling of data by integration of commonsense reasoning

Any intelligent problem solving system should be able, given the known data on a case, to decide whether some item of information is true, false or unknown. In this paper the way in which various forms of commonsense reasoning can be integrated to provide such decisions is described. To this end three structural types of knowledge defined over data, and four strategies for exploiting these structures, are identified. ‘Decide-Status’ integrates the reasoning strategies into a task frame. This frame structure not only integrates the reasoning but also affords the appropriate facilities for providing strategic justifications for its conclusions, if required.     

The effects of case libraries on problem solving

Abstract The purpose of the study was to investigate the effects of providing access to a case library of related stories while undergraduates solved ill-structured problems. While solving complex food product development problems, the experimental group accessed experts' stories of similar, previously solved problems; the comparable group accessed fact sheets (expository representation of stories' content); and the control group accessed text selected at random from a textbook dealing with issues unrelated to the stories. On multiple-choice questions assessing processes related to problem solving (prediction, inferences, explanations, etc.), experimental students out-performed the comparable and control groups. Performance on short-answer questions also assessing problem-related skills was not significantly different, in part because of test fatigue. Analysis of interviews identified a number of factors that students used in deciding how to apply their study strategies, including causal factors, grounding phenomenon, grounding in context, and outcomes.