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pro vyhledávání: '"Seuken, A."'
We study the design of iterative combinatorial auctions (ICAs). The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, several papers have recently proposed machine learning (ML)-based
Externí odkaz:
http://arxiv.org/abs/2411.09355
The next frontier of online advertising is revenue generation from LLM-generated content. We consider a setting where advertisers aim to influence the responses of an LLM to align with their interests, while platforms seek to maximize advertiser valu
Externí odkaz:
http://arxiv.org/abs/2405.05905
Autor:
Curry, Michael, Thoma, Vinzenz, Chakrabarti, Darshan, McAleer, Stephen, Kroer, Christian, Sandholm, Tuomas, He, Niao, Seuken, Sven
Dynamic mechanism design is a challenging extension to ordinary mechanism design in which the mechanism designer must make a sequence of decisions over time in the face of possibly untruthful reports of participating agents. Optimizing dynamic mechan
Externí odkaz:
http://arxiv.org/abs/2402.08129
Autor:
Friedrich, Paul, Zhang, Yulun, Curry, Michael, Dierks, Ludwig, McAleer, Stephen, Li, Jiaoyang, Sandholm, Tuomas, Seuken, Sven
Multi-Agent Path Finding (MAPF) involves determining paths for multiple agents to travel simultaneously and collision-free through a shared area toward given goal locations. This problem is computationally complex, especially when dealing with large
Externí odkaz:
http://arxiv.org/abs/2401.17044
Many real-world auctions are dynamic processes, in which bidders interact and report information over multiple rounds with the auctioneer. The sequential decision making aspect paired with imperfect information renders analyzing the incentive propert
Externí odkaz:
http://arxiv.org/abs/2312.13232
We present a best-response based algorithm for computing verifiable $\varepsilon$-perfect Bayesian equilibria for sequential auctions with combinatorial bidding spaces and incomplete information. Previous work has focused only on computing Bayes-Nash
Externí odkaz:
http://arxiv.org/abs/2312.04516
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence, 38(9) (2024) 9891-9900
We study the design of iterative combinatorial auctions (ICAs). The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, several papers have recently proposed machine learning (ML)-based
Externí odkaz:
http://arxiv.org/abs/2308.10226
We study the course allocation problem, where universities assign course schedules to students. The current state-of-the-art mechanism, Course Match, has one major shortcoming: students make significant mistakes when reporting their preferences, whic
Externí odkaz:
http://arxiv.org/abs/2210.00954
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence Vol 37 (2023)
We study the combinatorial assignment domain, which includes combinatorial auctions and course allocation. The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, several papers have rec
Externí odkaz:
http://arxiv.org/abs/2208.14698
Combinatorial auctions are used to allocate resources in domains where bidders have complex preferences over bundles of goods. However, the behavior of bidders under different payment rules is not well understood, and there has been limited success i
Externí odkaz:
http://arxiv.org/abs/2206.03857