Motivation of participants in crowdsourcing platforms using intelligent agents

Abstract

Crowdsourcing is a model where individuals or organizations receive services from a large group of Internet users including ideas, finances, completing a complex task, etc. Several crowdsourcing websites have failed due to lack of user participation; hence, the success of crowdsourcing platforms is manifested by the mass of user participation. However, an issue of motivating users to participate in crowdsourcing platform stays challenging. We have proposed a new approach, i.e., reinforcement learning-based gamification method to motivate users. Gamification has been a practical approach to engaging users in many fields, but still, it needs an improvement in the Crowdsourcing platform. In this paper, the gamification approach is strengthened by a reinforcement learning algorithm. We have created an intelligent agent using the Reinforcement learning algorithm (Q-learning). This agent suggests an optimal action plan that yields maximum reward points to the users for their active participation in the Crowdsourcing application. Also, its performance is compared with the SARSA algorithm (On-policy learning), which is another Reinforcement learning algorithm.