000			06441nam a2200793 i 4500
001			6813489
003			IEEE
005			20200413152903.0
006			m eo d
007			cr cn |||m|||a
008			110723s2011 caua foab 000 0 eng d
020			_a9781608455171 (electronic bk.)
020			_z9781608455164 (pbk.)
024	7		_a10.2200/S00371ED1V01Y201107AIM013 _2doi
035			_a(CaBNVSL)gtp00548852
035			_a(OCoLC)743307093
040			_aCaBNVSL _cCaBNVSL _dCaBNVSL
050		4	_aQ320 _b.L284 2011
082	0	4	_a001.532 _222
100	1		_aLaw, Edith.
245	1	0	_aHuman computation _h[electronic resource] / _cEdith Law and Luis von Ahn.
260			_aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool, _cc2011.
300			_a1 electronic text (xi, 105 p.) : _bill., digital file.
490	1		_aSynthesis lectures on artificial intelligence and machine learning, _x1939-4616 ; _v# 13
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat Reader.
500			_aPart of: Synthesis digital library of engineering and computer science.
500			_aSeries from website.
504			_aIncludes bibliographical references (p. 77-103).
505	0		_aPreface -- Acknowledgments --
505	8		_a1. 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 --
505	8		_aPart 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 --
505	8		_a3. 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 --
505	8		_a4. 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 --
505	8		_aPart 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 --
505	8		_a6. 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 --
505	8		_a7. 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 --
505	8		_aBibliography -- Authors' biographies.
506	1		_aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510	0		_aCompendex
510	0		_aINSPEC
510	0		_aGoogle scholar
510	0		_aGoogle book search
520	3		_aHuman 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.
530			_aAlso available in print.
588			_aTitle from PDF t.p. (viewed on July 23, 2011).
650		0	_aBionics.
650		0	_aHuman-computer interaction.
650		0	_aMachine learning.
650		0	_aArtificial intelligence.
650		0	_aSocial media _xEconomic aspects.
653			_ahuman computation
653			_ahuman-in-the-loop algorithms
653			_aoutput aggregation
653			_aactive learning
653			_alatent class models
653			_atask routing
653			_alabor markets
653			_agames with a purpose
653			_atask design
653			_acrowdsourcing
653			_amechanism design
653			_aincentives
700	1		_aAhn, Luis von.
776	0	8	_iPrint version: _z9781608455164
830		0	_aSynthesis digital library of engineering and computer science.
830		0	_aSynthesis lectures on artificial intelligence and machine learning, _x1939-4616 ; _v# 13.
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6813489
999			_c561861 _d561861