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| Type | Conference or Workshop Paper |
| Scope | Learning and pedagogical Research |
| Published in Proceedings | Yes |
| Title | Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Page Range | 541 - 548 |
| Event Title | Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 |
| Event Type | conference |
| Event Location | Vienna, Austria |
| Event Start Date | July 23 - 2022 |
| Event End Date | July 29 - 2022 |
| Publisher | International Joint Conferences on Artificial Intelligence Organization |
| Abstract Text | Many important resource allocation problems involve the combinatorial assignment of items, e.g., auctions or course allocation. Because the bundle space grows exponentially in the number of items, preference elicitation is a key challenge in these domains. Recently, researchers have proposed ML-based mechanisms that outperform traditional mechanisms while reducing preference elicitation costs for agents. However, one major shortcoming of the ML algorithms that were used is their disregard of important prior knowledge about agents' preferences. To address this, we introduce monotone-value neural networks (MVNNs), which are designed to capture combinatorial valuations, while enforcing monotonicity and normality. On a technical level, we prove that our MVNNs are universal in the class of monotone and normalized value functions, and we provide a mixed-integer linear program (MILP) formulation to make solving MVNN-based winner determination problems (WDPs) practically feasible. We evaluate our MVNNs experimentally in spectrum auction domains. Our results show that MVNNs improve the prediction performance, they yield state-of-the-art allocative efficiency in the auction, and they also reduce the run-time of the WDPs. Our code is available on GitHub: https://github.com/marketdesignresearch/MVNN. |
| Free access at | Official URL |
| Official URL | https://arxiv.org/abs/2109.15117 |
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| Digital Object Identifier | 10.24963/ijcai.2022/77 |
| Other Identification Number | merlin-id:23343 |
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