The P-Box CDF-Intervals: A Reliable Constraint Reasoning with Quantifiable Information. Issue 4 (21st July 2014)

• Record Type: Journal Article
• Authors: SAAD, AYA; FRÜHWIRTH, THOM; GERVET, CARMEN; Leuschel, Michael; Schrijvers, Tom
• Is Part Of: Theory and practice of logic programming. Volume 14:Issue 4/5(2014)
• Page Start: 461
• Page End: 475
• Publication Date: 2014-07-21
• Subjects: Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068414000143
• Languages: English
• ISSNs: 1471-0684

Abstract

<abstract abstract-type="normal"> <title>Abstract</title> <p>This paper introduces a new constraint domain for reasoning about data with uncertainty. It extends convex modeling with the notion of p-box to gain additional quantifiable information on the data whereabouts. Unlike existing approaches, the p-box envelops an unknown probability instead of approximating its representation. The p-box bounds are uniform cumulative distribution functions (<italic>cdf</italic>) in order to employ linear computations in the probabilistic domain. The reasoning by means of p-box <italic>cdf</italic>-intervals is an interval computation which is exerted on the real domain then it is projected onto the <italic>cdf</italic> domain. This operation conveys additional knowledge represented by the obtained probabilistic bounds. The empirical evaluation of our implementation shows that, with minimal overhead, the output solution set realizes a full enclosure of the data along with tighter bounds on its probabilistic distributions.</p> </abstract>