TY - CHAP
T1 - Stereotype Reputation with Limited Observability
AU - Taylor, Phillip
AU - Griffiths, Nathan
AU - Barakat, Lina
AU - Miles, Simon
PY - 2017/11/25
Y1 - 2017/11/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85036647313&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-71682-4_6
DO - 10.1007/978-3-319-71682-4_6
M3 - Other chapter contribution
AN - SCOPUS:85036647313
SN - 9783319716817
VL - 10642 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 84
EP - 102
BT - Autonomous Agents and Multiagent Systems - AAMAS 2017 Workshops, Revised Selected Papers
PB - Springer Verlag
T2 - 16th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2017
Y2 - 8 May 2017 through 12 May 2017
ER -