Modelling and fusion of imperfect implication rules

Janith N. Heendeni; Kamal Premaratne; Manohar N. Murthi; Matthias Scheutz

doi:10.1007/978-3-319-11191-9_34

Modelling and fusion of imperfect implication rules

Janith N. Heendeni, Kamal Premaratne, Manohar N. Murthi, Matthias Scheutz

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper, we develop a method to find the uncertain consequent by fusing the uncertain antecedent and the uncertain implication rule. In particular with Dempster-Shafer theoretic models utilized to capture the uncertainty intervals associated with the antecedent and the rule itself, we derive bounds on the confidence interval associated with the rule consequent. We derive inequalities for the belief and plausibility values of the consequent and with least commitment choice they become equations. We also demonstrate the consistency of our model with probability and classical logic.

Original language	English (US)
Pages (from-to)	313-320
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8764
DOIs	https://doi.org/10.1007/978-3-319-11191-9_34
State	Published - 2014

Keywords

Antecedent
Belief
Consequent
Fusion
Implication rule
Plausibility
Uncertainty

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

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title = "Modelling and fusion of imperfect implication rules",

abstract = "In this paper, we develop a method to find the uncertain consequent by fusing the uncertain antecedent and the uncertain implication rule. In particular with Dempster-Shafer theoretic models utilized to capture the uncertainty intervals associated with the antecedent and the rule itself, we derive bounds on the confidence interval associated with the rule consequent. We derive inequalities for the belief and plausibility values of the consequent and with least commitment choice they become equations. We also demonstrate the consistency of our model with probability and classical logic.",

keywords = "Antecedent, Belief, Consequent, Fusion, Implication rule, Plausibility, Uncertainty",

author = "Heendeni, {Janith N.} and Kamal Premaratne and Murthi, {Manohar N.} and Matthias Scheutz",

note = "Funding Information: Acknowledgment. This work is based on research supported by the US Office of Naval Research (ONR) via grants #N00014-10-1-0140. and #N00014-11-1-0493, and the US National Science Foundation (NSF) via grant #1038257.",

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doi = "10.1007/978-3-319-11191-9_34",

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T1 - Modelling and fusion of imperfect implication rules

AU - Heendeni, Janith N.

AU - Premaratne, Kamal

AU - Murthi, Manohar N.

AU - Scheutz, Matthias

N1 - Funding Information: Acknowledgment. This work is based on research supported by the US Office of Naval Research (ONR) via grants #N00014-10-1-0140. and #N00014-11-1-0493, and the US National Science Foundation (NSF) via grant #1038257.

PY - 2014

Y1 - 2014

N2 - In this paper, we develop a method to find the uncertain consequent by fusing the uncertain antecedent and the uncertain implication rule. In particular with Dempster-Shafer theoretic models utilized to capture the uncertainty intervals associated with the antecedent and the rule itself, we derive bounds on the confidence interval associated with the rule consequent. We derive inequalities for the belief and plausibility values of the consequent and with least commitment choice they become equations. We also demonstrate the consistency of our model with probability and classical logic.

AB - In this paper, we develop a method to find the uncertain consequent by fusing the uncertain antecedent and the uncertain implication rule. In particular with Dempster-Shafer theoretic models utilized to capture the uncertainty intervals associated with the antecedent and the rule itself, we derive bounds on the confidence interval associated with the rule consequent. We derive inequalities for the belief and plausibility values of the consequent and with least commitment choice they become equations. We also demonstrate the consistency of our model with probability and classical logic.

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KW - Belief

KW - Consequent

KW - Fusion

KW - Implication rule

KW - Plausibility

KW - Uncertainty

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JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

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Modelling and fusion of imperfect implication rules

Abstract

Keywords

ASJC Scopus subject areas

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