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Stereotype Reputation with Limited Observability

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Phillip Taylor, Nathan Griffiths, Lina Barakat, Simon Miles

Original languageEnglish
Title of host publicationAutonomous Agents and Multiagent Systems - AAMAS 2017 Workshops, Revised Selected Papers
PublisherSpringer Verlag
Number of pages19
Volume10642 LNAI
ISBN (Print)9783319716817
Publication statusE-pub ahead of print - 25 Nov 2017
Event16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017 - Sao Paulo, Brazil
Duration: 8 May 201712 May 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10642 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017
CitySao Paulo

King's Authors


Assessing trust and reputation is essential in multi-agent systems where agents must decide who to interact with. Assessment typically relies on the direct experience of a trustor with a trustee agent, or on information from witnesses. Where direct or witness information is unavailable, such as when agent turnover is high, stereotypes learned from common traits and behaviour can provide this information. Such traits may be only partially or subjectively observed, with witnesses not observing traits of some trustees or interpreting their observations differently. Existing stereotype-based techniques are unable to account for such partial observability and subjectivity. In this paper we propose a method for extracting information from witness observations that enables stereotypes to be applied in partially and subjectively observable dynamic environments. Specifically, we present a mechanism for learning translations between observations made by trustor and witness agents with subjective interpretations of traits. We show through simulations that such translation is necessary for reliable reputation assessments in dynamic environments with partial and subjective observability.

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