Name
Abstract | ||
|---|---|---|
We show how machine learning and inference can be harnessed to leverage the complementary strengths of humans and computational agents to solve crowdsourcing tasks. We construct a set of Bayesian predictive models from data and describe how the models operate within an overall crowd-sourcing architecture that combines the efforts of people and machine vision on the task of classifying celestial bodies defined within a citizens' science project named Galaxy Zoo. We show how learned probabilistic models can be used to fuse human and machine contributions and to predict the behaviors of workers. We employ multiple inferences in concert to guide decisions on hiring and routing workers to tasks so as to maximize the efficiency of large-scale crowdsourcing processes based on expected utility. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.5555/2343576.2343643 | AAMAS |
Keywords | Field | DocType |
crowdsourcing task,complementary strength,computational agent,machine contribution,machine intelligence,celestial body,galaxy zoo,large-scale crowdsourcing,machine vision,expected utility,bayesian predictive model,crowdsourcing,probabilistic model,machine learning | Data science,Architecture,Machine vision,Computer science,Expected utility hypothesis,Crowdsourcing,Inference,Artificial intelligence,Probabilistic logic,Relevance vector machine,Machine learning,Bayesian probability | Conference |
ISBN | Citations | PageRank |
0-9817381-1-7 | 147 | 7.11 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ece Kamar | 1 | 577 | 48.11 |
| Severin Hacker | 2 | 199 | 11.78 |
| Eric Horvitz | 3 | 9402 | 1058.25 |