Appeared at: Autonomous Agents and MultiAgent Systems (AAMAS) 2022 Authors: SAMHITA KANAPARTHY, SANKARSHAN DAMLE, SUJIT GUJAR Crowdsourcing effectively solves many tasks by employing a distributed human population. Information aggregation from multiple reports provided by potentially unreliable or malicious agents is a primary challenge in crowdsourcing systems. As a result, research in this area has focused … Continue reading REFORM: Reputation Based Fair and Temporal Reward Framework for Crowdsourcing
Paper presented at IJCAI 2019, Macao, China.
A fair reward mechanism for location-based crowdsensing queries.