Abstract
Incentives are explored in the sharing economy to inspire users for better resource allocation. Previous works build a budget-feasible incentive mechanism to learn users' cost distribution. However, they only consider a special case that all tasks are considered as the same. The general problem asks for finding a solution when the cost for different tasks varies. In this paper, we investigate this general problem by considering a system with k levels of difficulty. We present two incentivizing strategies for offline and online implementation, and formally derive the ratio of utility between them in different scenarios. We propose a regret-minimizing mechanism to decide incentives by dynamically adjusting budget assignment and learning from users' cost distributions. Our experiment demonstrates utility improvement about 7 times and time saving of 54% to meet a utility objective compared to the previous works.
Keywords
- Agent-based and Multi-agent Systems: Economic Paradigms, Auctions and Market-Based Systems
- Multidisciplinary Topics and Applications: Transportation
- Machine Learning: Reinforcement Learning
- Machine Learning Applications: Applications of Reinforcement Learning
