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Human-agent collectives

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A. RogersAuthors Info & Claims

Communications of the ACM, Volume 57, Issue 12

Pages 80 - 88

https://doi.org/10.1145/2629559

Published: 26 November 2014 Publication History

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Abstract

HACs offer a new science for exploring the computational and human aspects of society.

References

[1]

Abowd, G.D., Ebling, M., Hung, G., Lei, H., and Gellersen, H.W. Context-aware computing. IEEE Pervasive Computing 1, 3 (2002) 22--23.

Digital Library

Google Scholar

[2]

Ariely, D., Bracha, A. and Meier, S. Doing good or doing well? Image motivation and monetary incentives in behaving prosocially. American Economic Review 99, 1 (2007), 544--55.

Google Scholar

[3]

Buneman, P., Cheney, J. and Vansummeren, S. On the expressiveness of implicit provenance in query and update languages. ACM Trans. Database Systems 33, 4 (2008), 1--47.

Digital Library

Google Scholar

[4]

Dash, R.K., Parkes, D.C. and Jennings, N.R. Computational mechanism design: A call to arms. IEEE Intelligent Systems 18, 6 (2003) 40--47.

Digital Library

Google Scholar

[5]

Ebden, M., Huynh, T.D., Moreau, L., Ramchurn, S.D. and Roberts, S.J. Network analysis on provenance graphs from a crowdsourcing application. In Proc. 4^th Int. Conf. on Provenance and Annotation of Data and Processes. (Santa Barbara, CA, 2012), 168--182.

Digital Library

Google Scholar

[6]

Fong, T., Nourbaksha, I. and Dautenhahn, K. A survey of socially interactive robots. Robots and Autonomous Systems 42 (2003), 143--166.

Crossref

Google Scholar

[7]

Gil, Y., Deelman, E., Ellisman, M., Fahringer, T., Fox, G., Gannon, D., Goble, C., Livny, M., Moreau, L. and Myers, J. Examining the challenges of scientific workflows. IEEE Computer 40, 12 (2007), 26--34.

Digital Library

Google Scholar

[8]

Horvitz, E. Principles of mixed-initiative user interfaces. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems (New York, NY, 1999), 159--166.

Digital Library

Google Scholar

[9]

Jennings, N.R. An agent-based approach for building complex software systems. Commun. ACM 44, 4 (Apr. 2001) 35--41.

Digital Library

Google Scholar

[10]

Kahneman, D. Maps of bounded rationality: Psychology for behavioral economics. American Economic Review 93, 5 (2003) 1449--1475.

Crossref

Google Scholar

[11]

Kamar, E., Gal, Y. and Grosz, B. Modeling information exchange opportunities for effective human-computer teamwork. Artificial Intelligence Journal 195, 1 (2013) 528--555.

Digital Library

Google Scholar

[12]

Kifor, T. et al. Provenance in agent-mediated healthcare systems. IEEE Intelligent Systems 21, 6, (2006) 38--46.

Digital Library

Google Scholar

[13]

Krause, A., Horvitz, E., Kansal, A. and Zhao, F. Toward community sensing. In Proc. Int. Conf. on Information Processing in Sensor Networks (St Louis, MO, 2008), 481--492.

Digital Library

Google Scholar

[14]

Luger, E. and Rodden, T. An informed view on consent for UbiComp. In Proc. Int. Joint Conf. on Pervasive and Ubiquitous Computing (2013), 529--538.

Digital Library

Google Scholar

[15]

Maes, P. Agents that reduce work and information overload. Commun. ACM 37, 7 (1994), 31--40.

Digital Library

Google Scholar

[16]

Manson, N.C. and O'Neill, O. Rethinking informed consent in bioethics. CUP, 2007.

Crossref

Google Scholar

[17]

Michalak, T., Aaditha, K.V., Szczepanski, P., Ravindran, B. and Jennings, N.R. Efficient computation of the Shapley value for game-theoretic network centrality. J. AI Research 46 (2013), 607--650.

Digital Library

Google Scholar

[18]

Moran, S., Pantidi, N., Bachour, K., Fischer, J.E., Flintham, M. and Rodden, T. Team reactions to voiced agent instructions in a pervasive game. In Proc. Int. Conf. on Intelligent User Interfaces, (Santa Monica, CA, 2013), 371--382.

Digital Library

Google Scholar

[19]

Moreau, L. The foundations for provenance on the Web. Foundations and Trends in Web Science 2, 2--3 (2010) 99--241.

Digital Library

Google Scholar

[20]

Moreau, L. et al. Prov-dm: The prov data model. W3C Recommendation REC-prov-dm-20130430, World Wide Web Consortium, 2013.

Google Scholar

[21]

Naroditskiy, V., Rahwan, I., Cebrian, M. and Jennings, N.R. Verification in referral-based crowdsourcing. PLoS ONE 7, 10 (2012) e45924.

Crossref

Google Scholar

[22]

Nissenbaum, H. Privacy as contextual integrity. Washington Law Review 79, 1 (2004), 119--158.

Google Scholar

[23]

Palen, L. and Dourish, P. Unpacking privacy for a networked world. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2003), 129--136.

Digital Library

Google Scholar

[24]

Paxton, M. and Benford, S. Experiences of participatory sensing in the wild. In Proc. 11^th Int. Conf. on Ubiquitous Computing, (Orlando, FL, 2009), 265--274.

Digital Library

Google Scholar

[25]

Rahwan, T., Ramchurn, S.D., Jennings, N.R. and Giovannucci, A. An anytime algorithm for optimal coalition structure generation. J. Artificial Intelligence Research 34 (2009), 521--567.

Digital Library

Google Scholar

[26]

Rahwan, I. et al. Global manhunt pushes the limits of social mobilization. IEEE Computer 46, 4 (2013a) 68--75.

Digital Library

Google Scholar

[27]

Rahwan, T., Nguyen, T-D, Michalak, T., Polukarov, M., Croitoru, M., Jennings, N.R. Coalitional games via network flows. In Proc. 23^rd Int. Joint Conf. on Artificial Intelligence, (Beijing, China, 2013b), 324--331.

Digital Library

Google Scholar

[28]

Reddy, S., Parker, A., Hyman, J., Burke, J., Estrin, D. and Hansen, M. Image browsing, processing, and clustering for participatory sensing. In Proc. 4^th Workshop on Embedded Networked Sensors, (Cork, Ireland, 2007), 13--17.

Digital Library

Google Scholar

[29]

Robu, V., Gerding, E.H., Stein, S., Parkes, D.C., Rogers, A. and Jennings, N.R. An online mechanism for multi-unit demand and its application to plug-in hybrid electric vehicle charging. J. Artificial Intelligence Research (2013).

Digital Library

Google Scholar

[30]

Rogers, A., Farinelli, A., Stranders, R. and Jennings, N.R. Bounded approximate decentralised coordination via the max-sum algorithm. Artificial Intelligence 175, 2 (2011), 730--759.

Digital Library

Google Scholar

[31]

Scekic, O., Truong, H.-L. and Dustdar, S. Incentives and rewarding in social computing. Commun. ACM 56, 6 (2013), 72--82.

Digital Library

Google Scholar

[32]

Simpson, E., Roberts, S.J., Smith, A. and Lintott, C. Bayesian combination of multiple, imperfect classifiers. In Proc. 25^th Conf. on Neural Information Processing Systems, (Granada, Spain, 2011).

Google Scholar

[33]

Steeves, V. Reclaiming the social value of privacy. Lessons from the Identity Trail. I. Kerr, V. Steeves and C. Lucock, eds. Oxford University Press, 2009, 193--208.

Google Scholar

[34]

Stone, P., Kaminka, G.A., Kraus, S. and Rosenschein, J.S. Ad hoc autonomous agent teams: Collaboration without pre-coordination. In Proc. 24^th Conference on Artificial Intelligence (2010).

Google Scholar

[35]

Tambe, M. et al. Conflicts in teamwork: Hybrids to the rescue" In Proc. 4^th Int. Joint Conf. on Autonomous Agents and Multiagent Systems, (Utrecht, The Netherlands, 2005), 3--10.

Digital Library

Google Scholar

[36]

Thaler, R.H. and Cass, R.S. Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press, 2008.

Google Scholar

[37]

Tran-Thanh, L., Venanzi, M., Rogers, A. and Jennings, N.R. Efficient budget allocation with accuracy guarantees for crowdsourcing classification tasks. In Proc. 12^th Int. Conf. on Autonomous Agents and Multi-Agent Systems (St. Paul, MN, 2013), 901--908.

Digital Library

Google Scholar

[38]

von Ahn, L. et al. recaptcha: Human-based character recognition via web security measures. Science 321, 5895 (2008), 1465--1468.

Crossref

Google Scholar

[39]

von Ahn, L. and Dabbish, L. Labeling images with a computer game. In Proc. SIGCHI Conf. on Human Factors in Computing Systems, (Vienna, Austria, 2004), 319--326.

Digital Library

Google Scholar

[40]

Wooldridge, M.J. and Jennings, N.R. Intelligent agents: Theory and practice. Knowledge Engineering Review 10, 2 (1995) 115--152.

Crossref

Google Scholar

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Index Terms

Human-agent collectives
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
2. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

Communications of the ACM Volume 57, Issue 12

December 2014

92 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/2692965

Editor:
Moshe Y. Vardi
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

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Publication History

Published: 26 November 2014

Published in CACM Volume 57, Issue 12

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Abstract

References

Cited By

Index Terms

Recommendations

Interruptions in Human-Agent Interaction

From collective intentionality to intentional collectives: An ontological perspective

Entrainment in Human-Agent Text Communication

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