Coalition-based task assignment with priority-aware fairness in spatial crowdsourcing

Abstract

With the widespread use of networked and geo-positioned mobile devices, e.g., smartphones, Spatial Crowdsourcing (SC), which refers to the assignment of location-based tasks to moving workers, is drawing increasing attention. One of the critical issues in SC is task assignment that allocates tasks to appropriate workers. We propose and study a novel SC problem, namely Coalition-based Task Assignment (CTA), where the spatial tasks (e.g., home improvement and furniture installation) may require more than one worker (forming a coalition) to cooperate to maximize the overall rewards of workers. We design a greedy and an equilibrium-based CTA approach. The greedy approach forms a set of worker coalitions greedily for performing tasks and uses an acceptance probability to identify high-value task assignments. In the equilibrium-based approach, workers form coalitions in sequence and update their strategies (i.e., selecting a best-response task), to maximize their own utility (i.e., the reward of the coalition they belong to) until a Nash equilibrium is reached. Since the equilibrium obtained is not unique and optimal in terms of total rewards, we further propose a simulated annealing scheme to find a better Nash equilibrium. To achieve fair task assignments, we optimize the framework to distribute rewards fairly among workers in a coalition based on their marginal contributions and give workers who arrive first at the SC platform highest priority. Extensive experiments demonstrate the efficiency and effectiveness of the proposed methods on real and synthetic data.

Optimizing Crowdsourcing Task Assignment Policies Using Multi-Agent Reinforcement Learning in Stochastic Games

“…By comparing our proposed approach against these benchmark algorithms, we were able to evaluate its performance and effectiveness in tackling the challenges of our experimental setting. for better Nash equilibria with the assistance of the SA strategy [36], providing a better chance to achieve Nash equilibria with higher total returns. • Random strategy: With the Random strategy, an agent will randomly select any available action with equal probability to execute, which can better explore the environment in certain circumstances.…”