Abstract
Significant advances in artificial intelligence suggest that we will be using intelligent agents on a regular basis in the near future. This chapter discusses group cognition as a principle for designing collaborative AI. Group cognition is the ability to relate to other group members’ decisions, abilities, and beliefs. It thereby allows participants to adapt their understanding and actions to reach common objectives. Hence, it underpins collaboration. We review two concepts in the context of group cognition that could inform the development of AI and automation in pursuit of natural collaboration with humans: conversational grounding and theory of mind. These concepts are somewhat different from those already discussed in AI research. We outline some new implications for collaborative AI, aimed at extending skills and solution spaces and at improving joint cognitive and creative capacity.
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References
Abbeel, P., Ng, A.Y.: Apprenticeship learning via inverse reinforcement learning. In: Proceedings of the twenty-first international conference on Machine learning, p. 1. ACM (2004)
Abrams, D., Rutland, A., Palmer, S.B., Pelletier, J., Ferrell, J., Lee, S.: The role of cognitive abilities in children’s inferences about social atypicality and peer exclusion and inclusion in intergroup contexts. Br. J. Dev. Psychol. 32(3), 233–247 (2014)
Akkerman, S., Van den Bossche, P., Admiraal, W., Gijselaers, W., Segers, M., Simons, R.J., Kirschner, P.: Reconsidering group cognition: from conceptual confusion to a boundary area between cognitive and socio-cultural perspectives? Educ. Res. Rev. 2(1), 39–63 (2007)
Alexakos, C., Kalogeras, A.P.: Internet of things integration to a multi agent system based manufacturing environment. In: 2015 IEEE 20th Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8. IEEE (2015)
Allen, J., Guinn, C.I., Horvitz, E.: Mixed-initiative interaction. IEEE Intell. Syst. Appl. 14(5), 14–23 (1999)
Argall, B.D., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robot. Auton. Syst. 57(5), 469–483 (2009)
Baker, M.J.: Collaboration in collaborative learning. Interact. Stud. 16(3), 451–473 (2015)
Baker, M., Hansen, T., Joiner, R., Traum, D.: The role of grounding in collaborative learning tasks. Collab. Learn. Cogn. Comput. Approach. 31, 63 (1999)
Bradáč, V., Kostolányová, K.: Intelligent tutoring systems. In: E-Learning, E-Education, and Online Training: Third International Conference, eLEOT 2016, Dublin, Ireland, August 31–September 2, 2016, Revised Selected Papers, pp. 71–78. Springer (2017)
Cai, Z., Wu, Q., Huang, D., Ding, L., Yu, B., Law, R., Huang, J., Fu, S.: Cognitive state recognition using wavelet singular entropy and arma entropy with afpa optimized gp classification. Neurocomputing 197, 29–44 (2016)
Cambria, E., White, B.: Jumping nlp curves: a review of natural language processing research. IEEE Comput. Intell. Mag. 9(2), 48–57 (2014)
Campbell, A., Wu, A.S.: Multi-agent role allocation: issues, approaches, and multiple perspectives. Auton. Agent. Multi-Agent Syst. 22(2), 317–355 (2011)
Cannon-Bowers, J.A., Salas, E.: Reflections on shared cognition. J. Organ. Behav. 22(2), 195–202 (2001)
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N.: Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1721–1730. ACM (2015)
Chandrasekaran, A., Yadav, D., Chattopadhyay, P., Prabhu, V., Parikh, D.: It takes two to tango: towards theory of ai’s mind (2017). arXiv:1704.00717
Chau, D.H., Kittur, A., Hong, J.I., Faloutsos, C.: Apolo: making sense of large network data by combining rich user interaction and machine learning. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 167–176. ACM (2011)
Cheng, J., Greiner, R.: Learning bayesian belief network classifiers: algorithms and system. In: Advances in artificial intelligence, pp. 141–151 (2001)
Chrislip, D.D., Larson, C.E.: Collaborative leadership: how citizens and civic leaders can make a difference, vol. 24. Jossey-Bass Inc Pub (1994)
Clark, H.H., Wilkes-Gibbs, D.: Referring as a collaborative process. Cognition 22(1), 1–39 (1986)
Clark, H.H., Brennan, S.E., et al.: Grounding in communication. Perspect. Soc. Shar. Cogn. 13(1991), 127–149 (1991)
Cohen, P.R., Perrault, C.R.: Elements of a plan-based theory of speech acts. Cogn. Sci. 3(3), 177–212 (1979)
Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio Speech Lang. Process. 20(1), 30–42 (2012)
Dartnall, T.: Artificial intelligence and creativity: an interdisciplinary approach, vol. 17. Springer Science & Business Media (2013)
de Haan, M.: Intersubjectivity in models of learning and teaching: reflections from a study of teaching and learning in a mexican mazahua community. In: The theory and practice of cultural-historical psychology, pp. 174–199 (2001)
De Jong, K.A., Spears, W.M., Gordon, D.F.: Using genetic algorithms for concept learning. Mach. Learn. 13(2–3), 161–188 (1993)
Deterding, C.S., Hook, J.D., Fiebrink, R., Gow, J., Akten, M., Smith, G., Liapis, A., Compton, K.: Mixed-initiative creative interfaces (2017)
Dresner, K., Stone, P.: A multiagent approach to autonomous intersection management. J. Artif. Intell. Res. 31, 591–656 (2008)
El Kaliouby, R., Robinson, P.: Mind reading machines: automated inference of cognitive mental states from video. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, vol. 1, pp. 682–688. IEEE (2004)
El Kaliouby, R., Robinson, P.: Real-time inference of complex mental states from facial expressions and head gestures. In: Real-Time Vision for Human-Computer Interaction, pp. 181–200. Springer (2005)
Emojis as content within chatbots and nlps (2016). https://www.smalltalk.ai/blog/2016/12/9/how-to-use-emojis-as-content-within-chatbots-and-nlps
Engel, D., Woolley, A.W., Jing, L.X., Chabris, C.F., Malone, T.W.: Reading the mind in the eyes or reading between the lines? Theory of mind predicts collective intelligence equally well online and face-to-face. PloS one 9(12), e115,212 (2014)
Flavell, J.H.: Theory-of-mind development: retrospect and prospect. Merrill-Palmer Q. 50(3), 274–290 (2004)
Fotheringham, M.J., Owies, D., Leslie, E., Owen, N.: Interactive health communication in preventive medicine: internet-based strategies in teaching and research. Am. J. Prev. Med. 19(2), 113–120 (2000)
Fussell, S.R., Kiesler, S., Setlock, L.D., Yew, V.: How people anthropomorphize robots. In: 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 145–152. IEEE (2008)
Galegher, J., Kraut, R.E., Egido, C.: Intellectual Teamwork: Social and Technological Foundations of Cooperative Work. Psychology Press (2014)
Goldstone, R.L., Theiner, G.: The multiple, interacting levels of cognitive systems (milcs) perspective on group cognition. Philos. Psychol. 30(3), 334–368 (2017)
Graesser, A.C., VanLehn, K., Rosé, C.P., Jordan, P.W., Harter, D.: Intelligent tutoring systems with conversational dialogue. AI Mag. 22(4), 39 (2001)
Gray, B.: Collaborating: Finding Common Ground for Multiparty Problems (1989)
Guzman, A.L.: The messages of mute machines: human-machine communication with industrial technologies. Communication+ 1 5(1), 1–30 (2016)
Hendricks, L.A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., Darrell, T.: Generating visual explanations. In: European Conference on Computer Vision, pp. 3–19. Springer (2016)
Hill, J., Ford, W.R., Farreras, I.G.: Real conversations with artificial intelligence: a comparison between human-human online conversations and human-chatbot conversations. Comput. Hum. Behav. 49, 245–250 (2015)
Hollan, J., Hutchins, E., Kirsh, D.: Distributed cognition: toward a new foundation for human-computer interaction research. ACM Trans. Comput.-Hum. Interact. (TOCHI) 7(2), 174–196 (2000)
Holzinger, A.: Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inform. 3(2), 119–131 (2016)
Holzinger, A., Plass, M., Holzinger, K., Crişan, G.C., Pintea, C.M., Palade, V.: Towards interactive machine learning (iml): applying ant colony algorithms to solve the traveling salesman problem with the human-in-the-loop approach. In: International Conference on Availability, Reliability, and Security, pp. 81–95. Springer (2016)
Hong, H.Y., Chen, F.C., Chai, C.S., Chan, W.C.: Teacher-education students views about knowledge building theory and practice. Instr. Sci. 39(4), 467–482 (2011)
Huber, G.P., Lewis, K.: Cross-understanding: implications for group cognition and performance. Acad. Manag. Rev. 35(1), 6–26 (2010)
iOS Siri, A.: Apple (2013)
Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (2014)
Karami, A.B., Jeanpierre, L., Mouaddib, A.I.: Human-robot collaboration for a shared mission. In: Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction, pp. 155–156. IEEE Press (2010)
Kelley, R., Wigand, L., Hamilton, B., Browne, K., Nicolescu, M., Nicolescu, M.: Deep networks for predicting human intent with respect to objects. In: Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction, pp. 171–172. ACM (2012)
Koch, J.: Design implications for designing with a collaborative ai. In: AAAI Spring Symposium Series, Designing the User Experience of Machine Learning Systems (2017)
Kulesza, T., Burnett, M., Wong, W.K., Stumpf, S.: Principles of explanatory debugging to personalize interactive machine learning. In: Proceedings of the 20th International Conference on Intelligent User Interfaces, pp. 126–137. ACM (2015)
Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350(6266), 1332–1338 (2015)
Lala, D., Inoue, K., Milhorat, P., Kawahara, T.: Detection of social signals for recognizing engagement in human-robot interaction (2017). arXiv:1709.10257 [cs.HC]
Lang, F., Fink, A.: Collaborative machine scheduling: challenges of individually optimizing behavior. Concurr. Comput. Pract. Exp. 27(11), 2869–2888 (2015)
Lave, J., Wenger, E.: Situated Learning: Legitimate Peripheral Participation. Cambridge university press, Cambridge (1991)
Lee, D., Lee, J., Kim, E.K., Lee, J.: Dialog act modeling for virtual personal assistant applications using a small volume of labeled data and domain knowledge. In: Sixteenth Annual Conference of the International Speech Communication Association (2015)
Lei, T., Barzilay, R., Jaakkola, T.: Rationalizing neural predictions (2016). arXiv:1606.04155
Levine, S.J., Williams, B.C.: Concurrent plan recognition and execution for human-robot teams. In: ICAPS (2014)
Licklider, J.C.: Man-computer symbiosis. IRE Trans. Hum. Factors Electron. 1, 4–11 (1960)
Lipton, Z.C.: The mythos of model interpretability (2016). arXiv:1606.03490
Mavridis, N.: A review of verbal and non-verbal human-robot interactive communication. Robot. Auton. Syst. 63, 22–35 (2015)
Mohammed, S., Ringseis, E.: Cognitive diversity and consensus in group decision making: the role of inputs, processes, and outcomes. Organ. Behav. Hum. Decis. Process. 85(2), 310–335 (2001)
Nehaniv, C.L., Dautenhahn, K., Kubacki, J., Haegele, M., Parlitz, C., Alami, R.: A methodological approach relating the classification of gesture to identification of human intent in the context of human-robot interaction. In: ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005, pp. 371–377. IEEE (2005)
Novak, J.: Mine, yours... ours? Designing for principal-agent collaboration in interactive value creation. Wirtschaftsinformatik 1, 305–314 (2009)
Oliver, N.M., Rosario, B., Pentland, A.P.: A bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831–843 (2000)
Pantic, M., Pentland, A., Nijholt, A., Huang, T.S.: Human computing and machine understanding of human behavior: a survey. In: Artifical Intelligence for Human Computing, pp. 47–71. Springer (2007)
Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you?: Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144. ACM (2016)
Rich, C., Ponsler, B., Holroyd, A., Sidner, C.L.: Recognizing engagement in human-robot interaction. In: 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 375–382. IEEE (2010)
Robert, S., Büttner, S., Röcker, C., Holzinger, A.: Reasoning under uncertainty: towards collaborative interactive machine learning. In: Machine Learning for Health Informatics, pp. 357–376. Springer (2016)
Robinson, T.N., Patrick, K., Eng, T.R., Gustafson, D., et al.: An evidence-based approach to interactive health communication: a challenge to medicine in the information age. JAMA 280(14), 1264–1269 (1998)
Roschelle, J., Teasley, S.D., et al.: The construction of shared knowledge in collaborative problem solving. Comput.-Support. Collab. Learn. 128, 69–197 (1995)
Ruttkay, Z., Reidsma, D., Nijholt, A.: Human computing, virtual humans and artificial imperfection. In: Proceedings of the 8th international conference on Multimodal interfaces, pp. 179–184. ACM (2006)
Sato, E., Yamaguchi, T., Harashima, F.: Natural interface using pointing behavior for human-robot gestural interaction. IEEE Trans. Industr. Electron. 54(2), 1105–1112 (2007)
Schurr, N., Marecki, J., Tambe, M., Scerri, P., Kasinadhuni, N., Lewis, J.P.: The future of disaster response: humans working with multiagent teams using defacto. In: AAAI Spring Symposium: AI Technologies for Homeland Security, pp. 9–16 (2005)
Shapiro, D., Shachter, R.: User-agent value alignment. In: Proceedings of The 18th National Conference on Artificial Intelligence AAAI (2002)
Sheridan, T.B.: Human-robot interaction: status and challenges. Hum. Factors 58(4), 525–532 (2016)
Shoham, Y., Leyton-Brown, K.: Multiagent systems: Algorithmic, game-theoretic, and logical foundations. Cambridge University Press, Cambridge (2008)
Sidner, C.L., Lee, C., Morency, L.P., Forlines, C.: The effect of head-nod recognition in human-robot conversation. In: Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction, pp. 290–296. ACM (2006)
Simard, P., Chickering, D., Lakshmiratan, A., Charles, D., Bottou, L., Suarez, C.G.J., Grangier, D., Amershi, S., Verwey, J., Suh, J.: Ice: enabling non-experts to build models interactively for large-scale lopsided problems (2014). arXiv:1409.4814
Soller, A.: Supporting social interaction in an intelligent collaborative learning system. Int. J. Artif. Intell. Educ. (IJAIED) 12, 40–62 (2001)
Stahl, G.: Shared meaning, common ground, group cognition. In: Group Cognition: Computer Support for Building Collaborative Knowledge, pp. 347–360 (2006)
Stahl, G.: From intersubjectivity to group cognition. Comput. Support. Coop. Work (CSCW) 25(4–5), 355–384 (2016)
Stone, P., Veloso, M.: Multiagent systems: a survey from a machine learning perspective. Auton. Robots 8(3), 345–383 (2000)
Taha, T., Miró, J.V., Dissanayake, G.: A pomdp framework for modelling human interaction with assistive robots. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 544–549. IEEE (2011)
Theiner, G., Allen, C., Goldstone, R.L.: Recognizing group cognition. Cogn. Syst. Res. 11(4), 378–395 (2010)
Turner, P.: Mediated Cognition. Springer International Publishing, Cham (2016)
Vondrick, C., Oktay, D., Pirsiavash, H., Torralba, A.: Predicting motivations of actions by leveraging text. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2997–3005 (2016)
Vondrick, C., Pirsiavash, H., Torralba, A.: Anticipating visual representations from unlabeled video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 98–106 (2016)
Wenger, E.: Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge. Morgan Kaufmann (2014)
Wood, D.J., Gray, B.: Toward a comprehensive theory of collaboration. J. Appl. Behav. Sci. 27(2), 139–162 (1991)
Woolf, B.P.: Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing e-Learning. Morgan Kaufmann (2010)
Yoshikawa, Y., Shinozawa, K., Ishiguro, H., Hagita, N., Miyamoto, T.: Responsive robot gaze to interaction partner. In: Robotics: Science and Systems (2006)
Yu, Z., Ramanarayanan, V., Lange, P., Suendermann-Oeft, D.: An open-source dialog system with real-time engagement tracking for job interview training applications. In: Proceedings of IWSDS (2017)
Zhang, S., Sridharan, M.: Active visual sensing and collaboration on mobile robots using hierarchical pomdps. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 1, pp. 181–188. International Foundation for Autonomous Agents and Multiagent Systems (2012)
Zhou, J., Chen, F.: Making machine learning useable. Int. J. Intell. Syst. Technol. Appl. 14(2), 91–109 (2015)
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The project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 637991).
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Koch, J., Oulasvirta, A. (2018). Group Cognition and Collaborative AI. In: Zhou, J., Chen, F. (eds) Human and Machine Learning. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-90403-0_15
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