Name
Abstract | ||
|---|---|---|
In this paper, we deal with the sequential decision making problem of agents operating in computational economies, where there is uncertainty regarding the trustworthiness of service providers populating the environment. Specifically, we propose a generic Bayesian trust model, and formulate the optimal Bayesian solution to the exploration-exploitation problem facing the agents when repeatedly interacting with others in such environments. We then present a computationally tractable Bayesian reinforcement learning algorithm to approximate that solution by taking into account the expected value of perfect information of an agent's actions. Our algorithm is shown to dramatically outperform all previous finalists of the international Agent Reputation and Trust (ART) competition, including the winner from both years the competition has been run. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.5555/1402298.1402329 | AAMAS (2) |
Keywords | Field | DocType |
generic bayesian trust model,optimal bayesian solution,expected value,exploration-exploitation problem,untrustworthy service provider,previous finalist,sequential decision,perfect information,international agent reputation,computationally tractable bayesian reinforcement,computational economy,reputation,service provider,uncertainty,trust,reinforcement learning | Bayesian reinforcement learning,Computer science,Trustworthiness,Service provider,Artificial intelligence,Expected value of perfect information,Machine learning,Reinforcement learning,Reputation,Bayesian probability | Conference |
Citations | PageRank | References |
22 | 0.96 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| W.T. Luke Teacy | 1 | 588 | 28.88 |
| Georgios Chalkiadakis | 2 | 400 | 40.00 |
| alex rogers | 3 | 2500 | 183.76 |
| Nicholas R. Jennings | 4 | 19348 | 1564.35 |