Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 77-103).

Preface -- Acknowledgments --

1. Introduction -- 1.1 Computation: now and then -- 1.2 What is human computation? -- 1.2.1 Explicit control -- 1.3 Tackling AI problems: from vision to biology -- 1.3.1 A matter of perception -- 1.3.2 The language barrier -- 1.3.3 Intuition into computationally intensive problems -- 1.4 Overview --

Part I. Solving computational problems -- 2. Human computation algorithms -- 2.1 A definition of algorithms -- 2.2 Building blocks of algorithms -- 2.2.1 Operations, controls and program synthesis -- 2.3 Programming frameworks -- 2.4 Evaluating human computation algorithms -- 2.4.1 Correctness -- 2.4.2 Efficiency -- 2.5 Summary --

3. Aggregating outputs -- 3.1 Objective versus cultural truth -- 3.2 Classification -- 3.2.1 Latent class models -- 3.2.2 Learning from imperfect data -- 3.3 Beyond classification -- 3.3.1 Ranking and voting -- 3.3.2 Clustering -- 3.3.3 Structured outputs -- 3.3.4 Beliefs -- 3.4 Summary --

4. Task routing -- 4.1 Push versus pull approaches -- 4.2 Push approach -- 4.2.1 Allocation -- 4.2.2 Matching -- 4.2.3 Inference -- 4.3 Pull approacH -- 4.3.1 Search and visualization -- 4.3.2 Task recommendation -- 4.3.3 Peer routing -- 4.4 Evaluation criteria -- 4.5 Summary --

Part II. Design -- 5. Understanding workers and requesters -- 5.1 Markets -- 5.1.1 Mechanical Turk and paid crowdsourcing -- 5.1.2 Security and access -- 5.1.3 Gamers -- 5.1.4 Citizen science -- 5.1.5 Learners -- 5.1.6 Temporary markets -- 5.2 Supporting end users -- 5.2.1 Workers -- 5.2.2 Requesters -- 5.3 Summary --

6. The art of asking questions -- 6.1 Designing tasks -- 6.1.1 Information -- 6.1.2 Granularity -- 6.1.3 Independence -- 6.1.4 Incentives -- 6.1.5 Quality control -- 6.2 Eliciting truthful responses -- 6.2.1 Human computation games -- 6.2.2 Leveraging communication -- 6.2.3 Explicitly preventing bad outputs -- 6.2.4 A brief survey of games and mechanisms -- 6.3 Summary -- Part III. Conclusion --

7. The future of human computation -- 7.1 Research directions -- 7.1.1 Interweaving human and machine intelligence -- 7.1.2 Fostering long-term relationships -- 7.1.3 Designing organizations and task markets -- 7.2 Conclusion --

Bibliography -- Authors' biographies.

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Human computation is a new and evolving research area that centers around harnessing human intelligence to solve computational problems that are beyond the scope of existing Artificial Intelligence (AI) algorithms. With the growth of the Web, human computation systems can now leverage the abilities of an unprecedented number of people via the Web to perform complex computation.There are various genres of human computation applications that exist today. Games with a purpose (e.g., the ESP Game) specifically target online gamers who generate useful data (e.g., image tags) while playing an enjoyable game. Crowdsourcing marketplaces (e.g., Amazon Mechanical Turk) are human computation systems that coordinate workers to perform tasks in exchange for monetary rewards. In identity verification tasks, users perform computation in order to gain access to some online content; an example is reCAPTCHA, which leverages millions of users who solve CAPTCHAs every day to correct words in books that optical character recognition (OCR) programs fail to recognize with certainty. This book is aimed at achieving four goals: (1) defining human computation as a research area; (2) providing a comprehensive review of existing work; (3) drawing connections to a wide variety of disciplines, including AI, Machine Learning, HCI, Mechanism/Market Design and Psychology, and capturing their unique perspectives on the core research questions in human computation; and (4) suggesting promising research directions for the future.

Also available in print.

Title from PDF t.p. (viewed on July 23, 2011).