Name
Abstract | ||
|---|---|---|
Evaluating the performance of an agent or group of agents can be, by itself, a very challenging problem. The stochastic nature of the environment plus the stochastic nature of agents' decisions can result in estimates with intractably large variances This paper examines the problem of finding low variance estimates of agent performance. In particular, we assume that some agent-environment dynamics are known, such as the random outcome of drawing a card or rolling a die. Other dynamics are unknown, such as the reasoning of a human or other black-box agent. Using the known dynamics, we describe the complete set of all unbiased estimators, that is, for any possible unknown dynamics the estimate's expectation is always the agent's expected utility. Then, given a belief abcut the unknown dynamics, we identify the unbiased estimator with minimum variance. If the belief is correct our estimate is optimal, and if the belief is wrong it is at least unbiased. Finally, we apply our unbiased estimator to the game of poker, demonstrating dramatically reduced variance and faster evaluation. |
Year | Venue | Keywords |
|---|---|---|
2006 | AAAI | possible unknown dynamic,intractably large variance,black-box agent,agent performance,optimal unbiased estimator,belief abcut,unknown dynamic,low variance estimate,stochastic nature,unbiased estimator,minimum variance,expected utility |
Field | DocType | Citations |
Efficient estimator,Best linear unbiased prediction,U-statistic,Minimum-variance unbiased estimator,Mathematical optimization,Stein's unbiased risk estimate,Unbiased rendering,Computer science,Lehmann–Scheffé theorem,Bias of an estimator | Conference | 12 |
PageRank | References | Authors |
1.16 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Zinkevich | 1 | 1893 | 160.99 |
| Michael H. Bowling | 2 | 2460 | 205.07 |
| Nolan Bard | 3 | 90 | 7.40 |
| Morgan Kan | 4 | 57 | 6.51 |
| Darse Billings | 5 | 406 | 45.35 |