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| Content Provider | ACM Digital Library |
|---|---|
| Author | Sajja, Neelima Sen, Sandip |
| Abstract | We consider the problem of user agents selecting processor agents to processor tasks. We assume that processor agents are drawn from two populations: high and low-performing processors with different averages but similar variance in performance. For selecting a processor, a user agent queries other user agents for their high/low rating of different processors. We assume that a known percentage of "liar" users, who give inverse estimates of processors. We develop a trust mechanism that determines the number of users to query given a target guarantee threshold likelihood of choosing high-performance processors in the face of such "noisy" reputation mechanisms. We evaluate the robustness of this reputation-based trusting mechanism over varying environmental parameters like percentage of liars, performance difference and variances for high and low-performing agents, learning rates, etc. |
| Starting Page | 288 |
| Ending Page | 293 |
| Page Count | 6 |
| File Format | |
| ISBN | 1581134800 |
| DOI | 10.1145/544741.544808 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2002-07-15 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Reputation and trust Social order Task allocation Control & norms |
| Content Type | Text |
| Resource Type | Article |
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