We propose a system for conducting an auction over locations in a continuous space. It enables participants to express their preferences over possible choices of location in the space, selecting the location that maximizes the total utility of all agents. We prevent agents from tricking the system into selecting a location that improves their individual utility at the expense of others by using a pricing rule that gives agents no incentive to misreport their true preferences. The system queries participants for their utility in many random locations, then trains a neural network to approximate the preference function of each participant. The parameters of these neural network models are transmitted and processed by the auction mechanism, which composes these into differentiable models that are optimized through gradient ascent to compute the final chosen location and charged prices.
|Title of host publication||Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021|
|Publisher||International Joint Conferences on Artificial Intelligence|
|Number of pages||4|
|State||Published - 2021|
|Event||30th International Joint Conference on Artificial Intelligence, IJCAI 2021 - Virtual, Online, Canada|
Duration: 19 Aug 2021 → 27 Aug 2021
|Name||IJCAI International Joint Conference on Artificial Intelligence|
|Conference||30th International Joint Conference on Artificial Intelligence, IJCAI 2021|
|Period||19/08/21 → 27/08/21|
Bibliographical notePublisher Copyright:
© 2021 International Joint Conferences on Artificial Intelligence. All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence