Similar articles

Abstract: The long-term goal of artificial intelligence (AI) is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. hybrid-augmented intelligence can be divided into two basic models: one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.

混合-增强智能：协作与认知

[1]Ando, R.K., 2007. Biocreative II gene mention tagging system at IBM Watson. Proc. 2nd BioCreative Challenge Evaluation Workshop, p.101-103.

[2]Ando, R.K., Dredze, M., Zhang, T., 2005. Trec 2005 genomics track experiments at IBM Watson. 14th Text REtrieval Conf., p.1-10.

[3]Atif, Y., Mathew, S.S., 2015. Building a smart campus to support ubiquitous learning. J. Amb. Intell. Human. Comput., 6(2):1-16.

[4]Ball, M.O., Chen, C.Y., Hoffman, R., et al., 2001. Collaborative decision making in air traffic management: current and future research directions. it In: Bianco, L., Dell’Olmo, P., Odoni, A.R. (Eds.), New Concepts and Methods in Air Traffic Management. Springer Berlin Heidelberg, Berlin, Germany, p.17-30.

[5]Barnes, M.J., Chen, J.Y.C., Jentsch, F., et al., 2013. An overview of humans and autonomy for military environments: safety, types of autonomy, agents, and user interfaces. Proc. 10th Int. Conf. on Engineering Psychology and Cognitive Ergonomics: Applications and Services, p.243-252.

[6]Boman, I.L., Bartfai, A., 2015. The first step in using a robot in brain injury rehabilitation: patients’and health-care professionals’perspective. Disab. Rehab. Assist. Technol., 10(5):365-370.

[7]Bradley, A.P., 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Patt. Recogn., 30(7):1145-1159.

[8]Browne, C.B., Powley, E., Whitehouse, D., et al., 2012. A survey of Monte Carlo tree search methods. IEEE Trans. Comput. Intell. AI Games, 4(1):1-43.

[9]Campbell, M., Hoane, A.J.Jr., Hsu, F.H., 2002. Deep Blue. Artif. Intell., 134(1-2):57-83.

[10]Chen, D., Yuan, Z., Hua, G., et al., 2016. Multi-timescale collaborative tracking. IEEE Trans. Patt. Anal. Mach. Intell., 39(1):141-155.

[11]Chen, Y., Argentinis, J.D.E., Weber, G., 2016. IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research. Clin. Therap., 38(4):688-701.

[12]Cimbala, S.J., 2012. Artificial Intelligence and National Security. Lexington Books, Lanham, USA.

[13]Denton, E.L., Chintala, S., Fergus, R., et al., 2015. Deep generative image models using a Laplacian pyramid of adversarial networks. Proc. 28th Int. Conf. on Neural Information Processing Systems, p.1486-1494.

[14]de Rocquigny, E., Nicolas, D., Stefano, T., 2008. Uncertainty in Industrial Practice: a Guide to Quantitative Uncertainty Management. John Wiley & Sons, Hoboken, USA.

[15]Dias, M.G., Harris, P., 1988. The effect of make-believe play on deductive reasoning. Br. J. Devel. Psychol., 6(3):207-221.

[16]Dounias, G., 2003. Hybrid computational intelligence in medicine. Proc. Workshop on Intelligent and Adaptive Systems in Medicine.

[17]Eakin, H., Luers, A.L., 2006. Assessing the vulnerability of social-environmental systems. Ann. Rev. Environ. Resourc., 31:1-477.

[18]Ferreira, F.J., Crispim, V.R., Silva, A.X., 2010. Detection of drugs and explosives using neutron computerized tomography and artificial intelligence techniques. Appl. Rad. Isot., 68(6):1012-1017.

[19]Fire, A., Zhu, S.C., 2016. Learning perceptual causality from video. ACM Trans. Intell. Syst. Technol., 7(2):1-22.

[20]Fischbein, H., 2002. Intuition in Science and Mathematics: an Educational Approach. Springer Science & Business Media, Berlin, Germany.

[21]Fjellheim, R., Bratvold, R.R., Herbert, M.C., 2008. CODIO - collaborative decisionmaking in integrated operations. Intelligent Energy Conf. and Exhibition, p.1-7.

[22]Fogel, D.B., 1995. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. Wiley-IEEE Press.

[23]Freyd, J.J., 1983. Representing the dynamics of a static form. Memory Cogn., 11(4):342-346.

[24]Funahashi, K.I., Nakamura, Y., 1993. Approximation of dynamic systems by continuous-time recurrent neural networks. Neur. Netw., 6(6):801-806.

[25]Gil, Y., Greaves, M., Hendler, J., et al., 2014. Amplify scientific discovery with artificial intelligence. Science, 346(6206):171-172.

[26]Gilbert, G.R., Beebe, M.K., 2010. United States Department of Defense Research in Robotic Unmanned Systems for Combat Casualty Care. Report No. RTO-MP-HFM-182, Fort Detrick, Frederick, USA.

[27]Goodfellow, I.J., Shlens, J., Szegedy, C., 2014a. Explaining and harnessing adversarial examples. ePrint Archive, arXiv:1412.6572.

[28]Goodfellow, I.J., Pougetabadie, J., Mirza, M., et al., 2014b. Generative adversarial nets. Advances in Neural Information Processing Systems, p.2672-2680.

[29]Graves, A., Mohamed, A.R., Hinton, G., 2013. Speech recognition with deep recurrent neural networks. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, p.6645-6649.

[30]Graves, A., Wayne, G., Danihelka, I., 2014. Neural turing machines. ePrint Archive, arXiv:1410.5401.

[31]Graves, A., Wayne, G., Reynolds, M., et al., 2016. Hybrid computing using a neural network with dynamic external memory. Nature, 538(7626):471-476.

[32]Griffiths, T.L., Chater, N., Kemp, C., et al., 2010. Probabilistic models of cognition: exploring representations and inductive biases. Trends Cogn. Sci., 14(8):357-364.

[33]Guilford, J.P., 1967. The Nature of Human Intelligence. McGraw-Hill, New York, USA.

[34]Hagan, M.T., Demuth, H.B., Beale, M.H., et al., 2002. Neural Network Design. PWS Publishing Co., Boston, USA.

[35]Hilovska, K., Koncz, P., 2012. Application of artificial intelligence and data mining techniques to financial markets. ACTA VSFS, 6:62-76.

[36]Hiskens, I.A., Davy, R.J., 2001. Exploring the power flow solution space boundary. IEEE Trans. Power Syst., 16(3):389-395.

[37]Hoffman, R., 1998. Integer Programming Models for Ground-Holding in Air Traffic Flow Management. PhD Thesis, University of Maryland, College Park, USA.

[38]Holland, J.H., 1992. Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press.

[39]Honey, C.J., Thivierge, J.P., Sporns, O., 2010. Can structure predict function in the human brain? NeuroImage, 52(3):766-776.

[40]Hu, P., Zhou, S., Ding, W.Z., et al., 2010. The comprehensive measurement model of the member importance in social networks. Int. Conf. on Management and Service Science, p.1-4.

[41]Hu, P., Wen, C.L., Pan, D., 2013. The mutual relationship among external network, internal resource, and competitiveness of enterprises. Sci. Res. Manag., V(4):90-98 (in Chinese).

[42]Hughes, D., Camp, C., O’Hara, J., et al., 2016. Health resource use following robot-assisted surgery versus open and conventional laparoscopic techniques in oncology: analysis of English secondary care data for radical prostatectomy and partial nephrectomy. BJU Int., 117(6):940-947.

[43]Im, D.Y., Ryoo, Y.J., Kim, D.Y., et al., 2009. Unmanned driving of intelligent robotic vehicle. ISIS Proc. 10th Symp. on Advanced Intelligent Systems, p.213-216.

[44]Ioffe, A.D., 1979. Necessary and sufficient conditions for a local minimum. 3: second order conditions and augmented duality. SIAM J. Contr. Opt., 17(2):266-288.

[45]Janis, I.L., Mann, L., 1977. Decision Making: a Psychological Analysis of Conflict, Choice, and Commitment. Free Press, New York, USA.

[46]Jennings, N.R., 2000. On agent-based software engineering artificial intelligence. Artif. Intell., 117(2):277-296.

[47]Johnson, M., Bradshaw, J.M., Feltovich, P.J., et al., 2014. Coactive design: designing support for interdependence in joint activity. Electr. Eng. Math. Comput. Sci., 3(1):43-49.

[48]Johnson, S., Slaughter, V., Carey, S., 1998. Whose gaze will infants follow? The elicitation of gaze-following in 12-month-olds. Devel. Sci., 1(2):233-238.

[49]Jordan, M.I., 2016. On computational thinking, inferential thinking and data science. Proc. 28th ACM Symp. on Parallelism in Algorithms and Architectures, p.47.

[50]Kourtzi, Z., Kanwisher, N., 2000. Activation in human MT/MST by static images with implied motion. J. Cogn. Neurosci., 12(1):48-55.

[51]Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B., 2015. Human-level concept learning through probabilistic program induction. Science, 350(6266):1332-1338.

[52]Lake, B.M., Ullman, T.D., Tenenbaum, J.B., et al., 2016. Building machines that learn and think like people. Behav. Brain Sci., 22:1-101.

[53]Ledford, H., 2015. How to solve the world’s biggest problems. Nature, 525:308-311.

[54]Lewis, D.D., 1998. Naive (Bayes) at forty: the independence assumption in information retrieval. European Conf. on Machine Learning, p.4-15.

[55]Lillicrap, T.P., Hunt, J.J., Pritzel, A., et al., 2016. Continuous control with deep reinforcement learning. ePrint Archive, arXiv:1509.02971.

[56]Lippmann, R.P., 1987. An introduction to computing with neural nets. IEEE ASSP Mag., 4(2):4-22.

[57]Liyanage, J.P., 2012. Hybrid Intelligence Through Business Socialization and Networking: Managing Complexities in the Digital Era. IGI Global, Hershey, USA.

[58]Marchiori, D., Warglien, M., 2008. Predicting human interactive learning by regret-driven neural networks. Science, 319(5866):1111-1113.

[59]Marr, D., 1977. Artificial intelligence–-a personal view. Artif. Intell., 9(1):37-48.

[60]Martin, J., 2007. The Meaning of the 21st Century: a Vital Blueprint for Ensuring Our Future. Random House.

[61]McCarthy, J., Hayes, P.J., 1987. Some Philosophical Problems from the Standpoint of Artificial Intelligence. Morgan Kaufmann Publishers Inc., Burlington, USA.

[62]Michalski, R.S., Carbonell, J.G., Mitchell, T.M., 1984. Machine Learning: an Artificial Intelligence Approach. Springer Science & Business Media, Berlin, Germany.

[63]Mikolov, T., Karafiát, M., Burget, L., et al., 2010. Recurrent neural network based language model. Conf. of the Int. Speech Communication Association, p.1045-1048.

[64]Minsky, M., 1961. Steps toward artificial intelligence. Proc. IRE, 49(1):8-30.

[65]Mirza, M., Osindero, S., 2014. Conditional generative adversarial nets. ePrint Archive, arXiv:1411.1784.

[66]Mizumoto, M., 1982. Comparison of fuzzy reasoning methods. Fuzzy Sets Syst., 8(3):253-283.

[67]Mnih, V., Kavukcuoglu, K., Silver, D., et al., 2013. Playing Atari with deep reinforcement learning. ePrint Archive, arXiv:1312.5602.

[68]Mnih, V., Kavukcuoglu, K., Silver, D., et al., 2015. Human-level control through deep reinforcement learning. Nature, 518(7540):529-533.

[69]Moran, J., Desimone, R., 1985. Selective Attention Gates Visual Processing in the Extrastriate Cortex. MIT Press, Cambridge, USA.

[70]Muir, B.M., 1994. Trust in automation: part I. Theoretical issues in the study of trust and human intervention in automated systems. Ergonomics, 37(11):1905-1922.

[71]Nash, J.F., 1950. Equilibrium points in n-person games. PNAS, 36(1):48-49.

[72]Navigli, R., Ponzetto, S.P., 2012. Babelnet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell., 193(6):217-250.

[73]Newell, A., Simon, H.A., 1972. Human Problem Solving. Prentice-Hall, Englewood Cliffs, USA.

[74]Nilsson, N.J., 1965. Learning Machines: Foundations of Trainable Pattern-Classifying Systems. McGraw-Hill, New York, USA.

[75]Nissen, M.J., Bullemer, P., 1987. Attentional requirements of learning: evidence from performance measures. Cogn. Psychol., 19(1):1-32.

[76]Noh, H., Hong, S., Han, B., 2015. Learning deconvolution network for semantic segmentation. IEEE Int. Conf. on Computer Vision, p.1520-1528.

[77]Norman, K.A., O’Reilly, R.C., 2003. Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. Psychol. Rev., 110(4):611-646.

[78]Ogura, T., Yamada, J., Yamada, S.I., et al., 1989. A 20 kbit associative memory lSI for artificial intelligence machines. IEEE J. Sol.-State Circ., 24(4):1014-1020.

[79]O’Keefe, J., Nadel, L., 1978. The Hippocampus as a Cognitive Map. Clarendon Press, Oxford.

[80]O’Leary, D.E., 2013. Artificial intelligence and big data. IEEE Intell. Syst., 28(2):96-99.

[81]Pan, Y.H., 2016. Heading toward artificial intelligence 2.0. Engineering, 2(4):409-413.

[82]Park, C.C., Kim, G., 2015. Expressing an image stream with a sequence of natural sentences. Advances in Neural Information Processing Systems, p.73-81.

[83]Poole, D., Mackworth, A., Goebel, R., 1997. Computational Intelligence: a Logical Approach. Oxford University Press, Oxford, UK.

[84]Premack, D., Premack, A.J., 1997. Infants attribute value to the goal-directed actions of self-propelled objects. J. Cogn. Neurosci., 9(6):848-856.

[85]Pylyshyn, Z.W., 1984. Computation and Cognition: Toward a Foundation for Cognitive Science. The MIT Press, Cambridge, Massachusetts, USA.

[86]Rachlin, H., 2012. Making IBM’s computer, Watson, human. Behav. Anal., 35(1):1-16.

[87]Radford, A., Metz, L., Chintala, S., 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. ePrint Archive, arXiv:1511.06434.

[88]Rashevsky, N., 1964. Man-machine interaction in automobile driving. Prog. Biocybern., 42:188-200.

[89]Rasmussen, C.E., 2000. The infinite Gaussian mixture model. Advances in Neural Information Processing Systems, p.554-560.

[90]Rehder, B., Hastie, R., 2001. Causal knowledge and categories: the effects of causal beliefs on categorization, induction, and similarity. J. Exp. Psychol., 130(3):323-360.

[91]Russell, S.J., Norvig, P., 1995. Artificial Intelligence: a Modern Approach. Prentice Hall, Englewood Cliffs, USA.

[92]Salimans, T., Goodfellow, I., Zaremba, W., et al., 2016. Improved techniques for training gans. Advances in Neural Information Processing Systems, p.2226-2234.

[93]Salvi, C., Bricolo, E., Kounios, J., et al., 2016. Insight solutions are correct more often than analytic solutions. Think. Reason., 22(4):443-460.

[94]Samuel, A.L., 1988. Some studies in machine learning using the game of checkers. IBM J. Res. Dev., 44(1-2):206-226.

[95]Saripalli, S., Montgomery, J.F., Sukhatme, G., 2003. Visually guided landing of an unmanned aerial vehicle. IEEE Trans. Robot. Autom., 19(3):371-380.

[96]Saxe, R., Carey, S., 2006. The perception of causality in infancy. ACTA Psychol., 123(1-2):144-165.

[97]Schlottmann, A., Ray, E.D., Mitchell, A., et al., 2006. Perceived physical and social causality in animated motions: spontaneous reports and ratings. ACTA Psychol., 123(1-2):112-143.

[98]Schwartz, T., Zinnikus, I., Krieger, H.U., et al., 2016. Hybrid teams: flexible collaboration between humans, robots and virtual agents. German Conf. on Multiagent System Technologies, p.131-146.

[99]Selfridge, O.G., 1988. Pandemonium: a paradigm for learning. National Physical Laboratory Conf., p.511-531.

[100]Shader, R.I., 2016. Some reflections on IBM Watson and on women’s health. Clin. Therap., 38(1):1-2.

[101]Sharp, C.S., Shakernia, O., Sastry, S.S., 2001. A vision system for landing an unmanned aerial vehicle. IEEE Int. Conf. on Robotics & Automation, p.1720-1727.

[102]Shrivastava, P., 1995. Ecocentric management for a risk society. Acad. Manag. Rev., 20(1):118-137.

[103]Shuaibu, B.M., Norwawi, N.M., Selamat, M.H., et al., 2015. Systematic review of Web application security development model. Artif. Intell. Rev., 43(2):259-276.

[104]Silver, D., Huang, A., Maddison, C.J., et al., 2016. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587):484-489.

[105]Simon, H.A., 1969. The Sciences of the Artificial. MIT Press, Cambridge, USA.

[106]Son, D., Lee, J., Qiao, S., et al., 2014. Multifunctional wearable devices for diagnosis and therapy of movement disorders. Nat. Nanotechnol., 9(5):397-404.

[107]Sternberg, R.J., 1984. Beyond IQ: a triarchic theory of human intelligence. Br. J. Educat. Stud., 7(2):269-287.

[108]Sternberg, R.J., Davidson, J.E., 1983. Insight in the gifted. Educat. Psychol., 18(1):51-57.

[109]Stone, P., Brooks, R., Brynjolfsson, E., et al., 2016. Artificial Intelligence and Life in 2030. One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, USA.

[110]Sun, Y., Wang, X.G., Tang, X.O., 2014. Deep learning face representation from predicting 10,000 classes. IEEE Conf. on Computer Vision and Pattern Recognition, p.1891-1898.

[111]Szegedy, C., Zaremba, W., Sutskever, I., et al., 2013. Intriguing properties of neural networks. ePrint Archive, arXiv:1312.6199.

[112]Szolovits, P., Patil, R.S., Schwartz, W.B., 1988. Artificial intelligence in medical diagnosis. Ann. Int. Med., 108(1):80-87.

[113]Tenenbaum, J.B., Kemp, C., Griffiths, T.L., et al., 2011. How to grow a mind: statistics, structure, and abstraction. Science, 331(6022):1279-1285.

[114]Thielscher, M., 1997. Ramification and causality. Artif. Intell., 89(1-2):317-364.

[115]Thielscher, M., 2001. The qualification problem: a solution to the problem of anomalous models. Artif. Intell., 131(1-2):1-37.

[116]Thrun, S., Burgard, W., Fox, D., 1998. A probabilistic approach to concurrent mapping and localization for mobile robots. Mach. Learn., 5(3):253-271.

[117]Tolman, E.C., 1948. Cognitive maps in rats and men. Psychol. Rev., 55(4):189-208.

[118]Tremoulet, P.D., Feldman, J., 2000. Perception of animacy from the motion of a single object. Perception, 29(8):943-951.

[119]Tversky, A., Kahneman, D., 1983. Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment. Psychol. Rev., 90(4):293-315.

[120]van den Oord, A., Kalchbrenner, N., Kavukcuoglu, K., 2016. Pixel recurrent neural networks. ePrint Archive, arXiv:1601.06759.

[121]Varaiya, P., 1993. Smart car on smart roads: problems of control. IEEE Trans. Autom. Contr., 38(2):195-207.

[122]Waldrop, M.M., 2015. Autonomous vehicles: no drivers required. Nature, 518(7537):20-23.

[123]Walters, M.L., Koay, K.L., Syrdal, D.S., et al., 2013. Companion robots for elderly people: using theatre to investigate potential users’ views. IEEE Ro-Man, p.691-696.

[124]Wang, F.Y., 2004. Artificial societies, computational experiments, and parallel systems: a discussion on computational theory of complex social-economic systems. Compl. Syst. Compl. Sci., 1(4):25-35.

[125]Wang, F.Y., Wang, X., Li, L.X., et al., 2016. Steps toward parallel intelligence. IEEE/CAA J. Autom. Sin., 3(4):345-348.

[126]Wang, J.J., Ma, Y.Q., Chen, S.T., et al., 2017. Fragmentation knowledge processing and networked artificial. Seieat. Sin. Inform., 47(1):1-22.

[127]Wang, L.M., Xiong, Y.J., Wang, Z., et al., 2016. Temporal segment networks: towards good practices for deep action recognition. LNCS, 9912:20-36.

[128]Wei, P., Zheng, N.N., Zhao, Y.B., et al., 2013. Concurrent action detection with structural prediction. IEEE Int. Conf. on Computer Vision, p.3136-3143.

[129]Wei, P., Zhao, Y., Zheng, N., et al., 2016. Modeling 4D human-object interactions for joint event segmentation, recognition, and object localization. IEEE Trans. Softw. Eng.

[130]Williams, R.J., Zipser, D., 1989. A learning algorithm for continually running fully recurrent neural networks. Neur. Comput., 1(2):270-280.

[131]Williams, W.M., Sternberg, R.J., 1988. Group intelligence: why some groups are better than others. Intelligence, 12(4):351-377.

[132]Xiao, C.Y., Dymetman, M., Gardent, C., 2016. Sequence-based structured prediction for semantic parsing. Meeting of the Association for Computational Linguistics, p.1341-1350.

[133]Yau, S.S., Gupta, S.K.S., Karim, F., et al., 2003. Smart classroom: enhancing collaborative learning using pervasive computing technology. ASEE Annual Conf. and Exposition, p.13633-13642.

[134]Yegnanarayana, B., 1994. Artificial neural networks for pattern recognition. Sadhana, 19(2):189-238.

[135]Youseff, L., Butrico, M., da Silva, D., 2008. Toward a unified ontology of cloud computing. Grid Computing Environments Workshop, p.1-10.

[136]Zadeh, L.A., 1996. Fuzzy logic and approximate reasoning. In: Advances in Fuzzy Systems - Applications and Theory: Volume 6. Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems. World Scientific Publishing, Singapore, p.238-259.

[137]Zhao, Y.Y., Qin, B., Liu, T., 2010. Sentiment analysis. J. Softw., 21(8):1834-1848.

[138]Zheng, N.N., Tang, S.M., Cheng, H., et al., 2004. Toward intelligent driver-assistance and safety warning systems. IEEE Intell. Syst., 19(2):8-11.