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SOBE: Source behavior estimation for subjective opinions In multiagent systems

Title	SOBE: Source behavior estimation for subjective opinions In multiagent systems
Author	Şensoy, Murat, Kaplan, L., Mel, G. de, Gunes, Taha Doğan
Publication Date:	2016
Publication Place	- International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Subject	Trust, Agents, Fusion
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	1548-8403
Record ID	c885f1d3-fbd0-4178-832e-28da8eff8011
Library Location	Computer Science
Date	2016
Notes	the U.S. Army Research Laboratory ; the U.K. Ministry of Defence ; TÜBİTAK
Sample Text	In cooperative or hostile environments, agents communicate their subjective opinions about various phenomenon. However, sources of these opinions may not always be competent and honest but more likely erroneous or even malicious. Furthermore, malicious sources may adopt certain behaviors to mislead the decision maker in a specific way. Fortunately, the reports of such misleading sources are correlated to ground truth. In this work, we propose to learn statistically meaningful opinion transformations that represent various behaviors of information sources. Then, we exploit these transformations while fusing opinions from unreliable sources. We show that our approach can be used to determine set of transformations that may lead to more accurate estimation of the truth.

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SOBE: Source behavior estimation for subjective opinions In multiagent systems

Author Şensoy, Murat, Kaplan, L., Mel, G. de, Gunes, Taha Doğan

Publication Date 2016

Publication Place - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)

Subject Trust, Agents, Fusion

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 1548-8403

Record ID c885f1d3-fbd0-4178-832e-28da8eff8011

Library Location Computer Science

Date 2016

Notes the U.S. Army Research Laboratory ; the U.K. Ministry of Defence ; TÜBİTAK

Sample Text In cooperative or hostile environments, agents communicate their subjective opinions about various phenomenon. However, sources of these opinions may not always be competent and honest but more likely erroneous or even malicious. Furthermore, malicious sources may adopt certain behaviors to mislead the decision maker in a specific way. Fortunately, the reports of such misleading sources are correlated to ground truth. In this work, we propose to learn statistically meaningful opinion transformations that represent various behaviors of information sources. Then, we exploit these transformations while fusing opinions from unreliable sources. We show that our approach can be used to determine set of transformations that may lead to more accurate estimation of the truth.