Difference between revisions of "Certainty versus Actuality in Trust Networks"
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|Master name=Computational Intelligence and Selforganisation | |Master name=Computational Intelligence and Selforganisation | ||
|Student name=Nicolas Höning | |Student name=Nicolas Höning | ||
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|Project start date=2009/04/01 | |Project start date=2009/04/01 | ||
| − | |Project end date=2009/08/ | + | |Project end date=2009/08/27 |
|Supervisor=Martijn Schut | |Supervisor=Martijn Schut | ||
| − | |Second reader= | + | |Second reader=Guszti Eiben |
|Company=VU | |Company=VU | ||
|Thesis title=Discounting Experience in Referral Networks | |Thesis title=Discounting Experience in Referral Networks | ||
| − | |Finished= | + | |Finished=Yes |
| − | |Poster=Media: | + | |Poster=Media:Posternaam.pdf |
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===KIM 1: Abstract=== | ===KIM 1: Abstract=== | ||
''Presentation date: June 05, 2009'' | ''Presentation date: June 05, 2009'' | ||
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In the context of information systems, a disruptive environment demands a solution to a trade-off: How quickly should agents forget experience? If they cherish their memories, they can build their decisions on larger data sets; if they forget quickly, they can respond well to change. This task can be characterised as a decentralised learning problem and its solution highly depends on the environment. In this work, we establish a testbed to examine this problem by building on work by Hang et al (2008). We observe which forgetting patterns work best and what happens if agents choose their forgetting rate freely. | In the context of information systems, a disruptive environment demands a solution to a trade-off: How quickly should agents forget experience? If they cherish their memories, they can build their decisions on larger data sets; if they forget quickly, they can respond well to change. This task can be characterised as a decentralised learning problem and its solution highly depends on the environment. In this work, we establish a testbed to examine this problem by building on work by Hang et al (2008). We observe which forgetting patterns work best and what happens if agents choose their forgetting rate freely. | ||
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Latest revision as of 12:59, 26 August 2010
| has title::Discounting Experience in Referral Networks | |
|---|---|
status: finished
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| Master: | project within::Computational Intelligence and Selforganisation |
| Student name: | student name::Nicolas Höning |
| Dates | |
| Start | start date:=2009/04/01 |
| End | end date:=2009/08/27 |
| Supervision | |
| Supervisor: | Martijn Schut |
| Second reader: | has second reader::Guszti Eiben |
| Company: | has company::VU |
| Poster: | has poster::Media:Media:Posternaam.pdf |
Signature supervisor
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Abstract
KIM 1: Abstract
Presentation date: June 05, 2009
A recent stream of research (Josang, Singh, Wang) developed a certainty-based trust representation, which not only communicates as how likely a good experience with the trustee is estimated, but also on how much actual experience this opinion is based. Agents can then rely more on trust reports which contain more information, thus interpreting them as more certain. In an environment where service providers may change the quality of their service, agents now face a dilemma: They desire two things in a trust report, certainty and actuality, but those are antiproportional: To make a trust report more actual, old information has to be discounted.
This research will build upon work by Hang et al (2008), where a simple trust referral network was designed to test certainty-based trust referral algebras in the presence of dynamic service quality and misleading referrals. Very basic time-based discounting of information has been implemented, but the above mentioned dilemma has not been explored. I plan to extend the network design to examine the effect of discounting strategies of referrers on the network performance.
KIM 2: Abstract
Presentation date: August 27, 2009
In the context of information systems, a disruptive environment demands a solution to a trade-off: How quickly should agents forget experience? If they cherish their memories, they can build their decisions on larger data sets; if they forget quickly, they can respond well to change. This task can be characterised as a decentralised learning problem and its solution highly depends on the environment. In this work, we establish a testbed to examine this problem by building on work by Hang et al (2008). We observe which forgetting patterns work best and what happens if agents choose their forgetting rate freely.
