Name
Abstract | ||
|---|---|---|
We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features. Given a budget, our framework iteratively computes the most cost-effective elicitation questions in order to help the agents make a better group decision.We illustrate the viability of the framework with experiments on Amazon Mechanical Turk, which we use to estimate the cost of answering different types of elicitation questions. We compare the prediction accuracy of our framework when adopting various information criteria that evaluate the expected information gain from a question. Our experiments show carefully designed information criteria are much more efficient, i.e., they arrive at the correct answer using fewer queries, than randomly asking questions given the budget constraint. |
Year | Venue | DocType |
|---|---|---|
2018 | UNCERTAINTY IN ARTIFICIAL INTELLIGENCE | Conference |
Volume | Citations | PageRank |
abs/1805.05287 | 0 | 0.34 |
References | Authors | |
12 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhibing Zhao | 1 | 5 | 3.82 |
| Haoming Li | 2 | 0 | 0.68 |
| Junming Wang | 3 | 2 | 1.10 |
| Jeffrey O. Kephart | 4 | 2978 | 399.73 |
| Nicholas Mattei | 5 | 201 | 32.55 |
| Hui Su | 6 | 293 | 33.30 |
| Lirong Xia | 7 | 1034 | 86.84 |