On Real Reward Testing

We extend the traditional nonnegative reward testing with negative rewards. In this new testing framework, may preorder and must preorder are the inverse of each other. More surprisingly, it turns out that the real reward must testing is no more powerful than the nonnegative reward testing, at least...

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Bibliographic Details
Published inShanghai jiao tong da xue xue bao Vol. 16; no. 4; pp. 479 - 484
Main Author 杨伟忠 邓玉欣
Format Journal Article
LanguageEnglish
Published Heidelberg Shanghai Jiaotong University Press 01.08.2011
Subjects
Architecture
Computer Science
Electrical Engineering
Engineering
Failure
Inclusions
Inverse
Life Sciences
Materials Science
Mathematical analysis
Simulation
failure simulation
TP 301.2
probabilistic processes
nonnegative reward testing
real reward testing
Online AccessGet full text
ISSN1007-1172
1995-8188
DOI10.1007/s12204-011-1176-6

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Summary:We extend the traditional nonnegative reward testing with negative rewards. In this new testing framework, may preorder and must preorder are the inverse of each other. More surprisingly, it turns out that the real reward must testing is no more powerful than the nonnegative reward testing, at least for finite processes. In order to prove that result, we exploit an important property of failure simulation about the inclusion of the testing outcomes between two related processes.
Bibliography:probabilistic processes, real reward testing, nonnegative reward testing, failure simulation
We extend the traditional nonnegative reward testing with negative rewards. In this new testing framework, may preorder and must preorder are the inverse of each other. More surprisingly, it turns out that the real reward must testing is no more powerful than the nonnegative reward testing, at least for finite processes. In order to prove that result, we exploit an important property of failure simulation about the inclusion of the testing outcomes between two related processes.
31-1943/U
YANG Wei-zhong , DENG Yu-xin（MOE-MS Key Laboratory for Intelligent Computing and Intelligent Systems, Shanghai Jiaotong University, Shanghai 200240, China）
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ISSN:1007-1172
1995-8188
DOI:10.1007/s12204-011-1176-6