Decision Market Based Learning For Multi-agent Contextual Bandit Problems
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Date
2024-01-01
DOI
Open Access Location
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
International Foundation for Autonomous Agents and Multiagent Systems
Rights
© 2024 International Foundation for Autonomous Agents and Multiagent Systems
CC BY 4.0
CC BY 4.0
Abstract
Information is often stored in a distributed and proprietary form, and agents who own this information are often self-interested and require incentives to reveal it. Suitable mechanisms are required to elicit and aggregate such distributed information for decision-making. In this study, we use simulations to investigate the use of decision markets as mechanisms in a multi-agent learning system to aggregate distributed information for decision-making in a contextual bandit problem.
Description
Keywords
Multi-agent systems, Prediction markets, Federated learning
Citation
Wang W, Pfeiffer T. (2024). Decision Market Based Learning For Multi-agent Contextual Bandit Problems. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS. (pp. 2549-2551). International Foundation for Autonomous Agents and Multiagent Systems.