Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Designing games with a purpose
Communications of the ACM - Designing games with a purpose
A Game-Theoretic Analysis of Games with a Purpose
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
CHI '09 Extended Abstracts on Human Factors in Computing Systems
KissKissBan: a competitive human computation game for image annotation
Proceedings of the ACM SIGKDD Workshop on Human Computation
Community-based game design: experiments on social games for commonsense data collection
Proceedings of the ACM SIGKDD Workshop on Human Computation
Search war: a game for improving web search
Proceedings of the ACM SIGKDD Workshop on Human Computation
On formal models for social verification
Proceedings of the ACM SIGKDD Workshop on Human Computation
Financial incentives and the "performance of crowds"
Proceedings of the ACM SIGKDD Workshop on Human Computation
PhotoSlap: a multi-player online game for semantic annotation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Collabio: a game for annotating people within social networks
Proceedings of the 22nd annual ACM symposium on User interface software and technology
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So far computer cannot satisfyingly solve many tasks that are extremely easy for human, such as image recognition or common sense reasoning. A partial solution is to delegate algorithmically difficult computation task to human, called human computation. The Game with a Purpose (GWAP), in which computational task is transformed into a game, is perhaps the most popular form of human computation. A simplified adverse selection model for output-agreement/simultaneous-verification GWAP was built, using the ESP Game as example. The experiment results favored an adverse selection model over an moral hazard model. We were particularly interested in output quality of a GWAP affected by how players are matched with each other, and proposed capability-aligned matching (CAM) versus commonly-used random matching. The analysis showed that when compared with random mathcing, the CAM improved output quality. The experiment confirmed conclusions drawed from the analysis, and further pointed out that task-human matching scheme was as important as human-human matching scheme studied in this paper. The main contribution of this paper is the analysis and empirical evaluation of humanhuman matching scheme, showing that capability-aligned matching can improve quality of GWAP.