Abstract
As an interdisciplinary research approach, traditional cognitive science adopts mainly the experiment, induction, modeling, and validation paradigm. Such models are sometimes not applicable in cyber-physical-social-systems (CPSSs), where the large number of human users involves severe heterogeneity and dynamics. To reduce the decision-making conflicts between people and machines in human-centered systems, we propose a new research paradigm called parallel cognition that uses the system of intelligent techniques to investigate cognitive activities and functionals in three stages: descriptive cognition based on artificial cognitive systems (ACSs), predictive cognition with computational deliberation experiments, and prescriptive cognition via parallel behavioral prescription. To make iteration of these stages constantly on-line, a hybrid learning method based on both a psychological model and user behavioral data is further proposed to adaptively learn an individual’s cognitive knowledge. Preliminary experiments on two representative scenarios, urban travel behavioral prescription and cognitive visual reasoning, indicate that our parallel cognition learning is effective and feasible for human behavioral prescription, and can thus facilitate human-machine cooperation in both complex engineering and social systems.
摘要
作为一门交叉学科,传统的认知科学主要采用实验、归纳、建模和验证的研究范式。对于包含大量用户异质行为和动态特性的社会物理信息系统,此种建模方法有时并不适用。为减少复杂人机系统中的人–机决策冲突,提出采用智能技术与系统来考察认知活动和认知功能的建模范式——平行认知。该范式分为三个阶段:基于人工认知系统的描述认知、基于计算思维实验的预测认知以及基于行为交互引导的引导性认知。在此基础上,进一步提出由心理模型和用户行为数据混合驱动的学习方法,自适应地学习人类个体的认知决策知识,从而使得三个阶段能够持续在线迭代。在交通行为引导和视觉推理场景下的初步实验表明,平行认知学习对于人类的行为引导是可行且有效的,有利于提升复杂工程系统和复杂社会系统中的人机协同程度。
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Bi WJ, Feng Q, Qi KH, et al., 2017. Failure evolution analysis for complex human-machine system: a case for nuclear power system. 2nd Int Conf on Reliability Systems Engineering, p.1–8. https://doi.org/10.1109/ICRSE.2017.8030717
Brachman RJ, 2002. Systems that know what they’re doing. IEEE Intell Syst, 17(6):67–71. https://doi.org/10.1109/MIS.2002.1134363
Campitelli G, Gobet F, 2010. Herbert Simon’s decision-making approach: investigation of cognitive processes in experts. Rev Gener Psychol, 14(4):354–364. https://doi.org/10.1037/a0021256
Christensen WD, Hooker CA, 2000. An interactivist-constructivist approach to intelligence: self-directed anticipative learning. Phil Psychol, 13(1):5–45. https://doi.org/10.1080/09515080050002717
Cunningham ML, Regan M, 2015. Autonomous vehicles: human factors issues and future research. Australasian College of Road Safety Conf.
Fecteau S, Knoch D, Fregni F, et al., 2007. Diminishing risk-taking behavior by modulating activity in the prefrontal cortex: a direct current stimulation study. J Neurosci, 27(46):12500–12505. https://doi.org/10.1523/JNEUROSCI.3283-07.2007
Gallegati M, Palestrini A, Russo A, 2017. Introduction to Agent-Based Economics. Elsevier, Amsterdam, the Netherlands. https://doi.org/10.1016/C2015-0-00736-5
Gehring WJ, Willoughby AR, 2002. The medial frontal cortex and the rapid processing of monetary gains and losses. Science, 295(5563):2279–2282. https://doi.org/10.1126/science.1066893
Gover AR, Harper SB, Langton L, 2020. Anti-Asian hate crime during the COVID-19 pandemic: exploring the reproduction of inequality. Am J Crim Just, 45(4):647–667. https://doi.org/10.1007/s12103-020-09545-1
Hosseinali F, Alesheikh AA, Nourian F, 2015. Assessing urban land-use development: developing an agent-based model. KSCE J Civ Eng, 19(1):285–295. https://doi.org/10.1007/s12205-012-0367-5
Huang YZ, Edwards MJ, Rounis E, et al., 2005. Theta burst stimulation of the human motor cortex. Neuron, 45(2):201–206. https://doi.org/10.1016/j.neuron.2004.12.033
Huettel SA, Stowe CJ, Gordon EM, et al., 2006. Neural signatures of economic preferences for risk and ambiguity. Neuron, 49(5):765–775. https://doi.org/10.1016/j.neuron.2006.01.024
Huettel SA, Song AW, McCarthy G, 2009. Functional Magnetic Resonance Imaging. Sinauer Associates, Sunderland, USA.
Hunt LT, Kolling N, Soltani A, et al., 2012. Mechanisms underlying cortical activity during value-guided choice. Nat Neurosci, 15(3):470–476. https://doi.org/10.1038/nn.3017
Kim HM, Wang FY, 1994. Design of adaptive neuro-fuzzy controllers. Proc IEEE Int Conf on Systems, Man and Cybernetics, p.1809–1814. https://doi.org/10.1109/ICSMC.1994.400113
Knoch D, Pascual-Leone A, Meyer K, et al., 2006. Diminishing reciprocal fairness by disrupting the right prefrontal cortex. Science, 314(5800):829–832. https://doi.org/10.1126/science.1129156
Matsumoto M, Hikosaka O, 2009. Two types of dopamine neuron distinctly convey positive and negative motivational signals. Nature, 459(7248):837–841. https://doi.org/10.1038/nature08028
Medler DA, 1998. A brief history of connectionism. Neur Comput Surv, 1:61–101.
Miller GA, 2003. The cognitive revolution: a historical perspective. Trends Cogn Sci, 7(3):141–144. https://doi.org/10.1016/S1364-6613(03)00029-9
Minsky M, 1986. The Society of Mind. Simon and Schuster, New York, USA, p.308. Morse AF, Ziemke T, 2008. On the role(s) of modelling in cognitive science. Pragm Cogn, 16(1):37–56.
Newell A, Simon HA, 1976. Computer science as empirical inquiry: symbols and search. Commun ACM, 19(3):113–126. https://doi.org/10.1145/360018.360022
Nianogo RA, Arah OA, 2015. Agent-based modeling of noncommunicable diseases: a systematic review. Am J Publ Health, 105(3):e20–e31. https://doi.org/10.2105/AJPH.2014.302426
Palmer C, 2020. The Boeing 737 Max Saga: automating failure. Engineering, 6(1):2–3. https://doi.org/10.1016/j.eng.2019.11.002
Romo R, Salinas E, 2001. Touch and go: decision-making mechanisms in somatosensation. Ann Rev Neurosci, 24:107–137. https://doi.org/10.1146/annurev.neuro.24.1.107
Tsai HC, Zhang F, Adamantidis A, et al., 2009. Phasic firing in dopaminergic neurons is sufficient for behavioral conditioning. Science, 324(5930):1080–1084. https://doi.org/10.1126/science.1168878
Walsh V, Pascual-Leone A, 2003. Transcranial Magnetic Stimulation: a Neurochronometrics of Mind. MIT Press, Cambridge, USA. https://doi.org/10.7551/mitpress/6896.001.0001
Walters ML, Dautenhahn K, te Boekhorst R, et al., 2005. The influence of subjects’ personality traits on personal spatial zones in a human-robot interaction experiment. IEEE Int Workshop on Robot and Human Interactive Communication, p.347-352. https://doi.org/10.1109/ROMAN.2005.1513803
Wang FY, 1992. Building knowledge structure in neural nets using fuzzy logic. In: Jamshidi M (Ed.), Robotics and Manufacturing: Recent Trends in Research, Education and Applications. American Society of Mechanical Engineers Press, New York, USA.
Wang FY, 1999. CAST Lab: a Cyber-Social-Physical Approach for Traffic Control and Transportation Management. ICSEC Technical Report, #1999-12-1.
Wang FY, 2003. Integrated intelligent control and management for urban traffic systems. Proc IEEE Int Conf on Intelligent Transportation Systems, p.1313–1317. https://doi.org/10.1109/ITSC.2003.1252696
Wang FY, 2004. Parallel system methods for management and control of complex systems. Contr Dec, 19(5):485–489, 514 (in Chinese). https://doi.org/10.3321/j.issn:1001-0920.2004.05.002
Wang FY, 2010. The emergence of intelligent enterprises: from CPS to CPSS. IEEE Intell Syst, 25(4):85–88. https://doi.org/10.1109/MIS.2010.104
Wang FY, 2013. A framework for social signal processing and analysis: from social sensing networks to computational dialectical analytics. Sci China Inform Sci, 43(12):1598–1611 (in Chinese).
Wang FY, 2016. A True Scientific Thinker: in Memory of Professor Marvin Minsky, the Father of AI. http://blog.sciencenet.cn/blog-2374-962496.html (in Chinese).
Wang FY, 2018a. Building robots for parallel cognition: cognitive science in reflection and perspective. 3rd Int Conf on Cognitive Systems and Information Processing.
Wang FY, 2018b. Parallel cognition: review and perspective of cognitive science. Symp on Brain-Like Computing and Intelligence.
Wang FY, 2018c. Parallel cognition: towards the integration of knowledge and behavior in intelligent cognitive science and technology. 1st China Symp on Cognitive computing and Hybrid Intelligence.
Wang FY, 2018d. Spring buds in winter: a causerie on cognitive science. Intell Compl, 12(4):2–7.
Wang FY, 2020. Parallel economics: a new supply-demand philosophy via parallel organizations and parallel management. IEEE Trans Comput Soc Syst, 7(4):840–848. https://doi.org/10.1109/TCSS.2020.3012747
Wang FY, Kim HM, 1995. Implementing adaptive fuzzy logic controllers with neural networks: a design paradigm. J Intell Fuzzy Syst, 3(2):165–180. https://doi.org/10.3233/IFS-1995-3206
Wang FY, Wang YF, 2020. Parallel ecology for intelligent and smart cyber-physical-social systems. IEEE Trans Comput Soc Syst, 7(6):1318–1323. https://doi.org/10.1109/TCSS.2020.3044129
Wang FY, Ye PJ, Li JJ, 2019. Social intelligence: the way we interact, the way we go. IEEE Trans Comput Soc Syst, 6(6):1139–1146. https://doi.org/10.1109/TCSS.2019.2954920
Wen D, Yuan Y, Li XR, 2013. Artificial societies, computational experiments, and parallel systems: an investigation on a computational theory for complex socioeconomic systems. IEEE Trans Serv Comput, 6(2):177–185. https://doi.org/10.1109/TSC.2012.24
Wiener N, 1948. Cybernetics or Control and Communication in the Animal and the Machine. John Wiley & Sons, Inc., New York, USA.
Ye PJ, Wang X, 2018. Population synthesis using discrete copulas. IEEE 21st Int Conf on Intelligent Transportation Systems, p.479–484. https://doi.org/10.1109/ITSC.2018.8570021
Ye PJ, Hu XL, Yuan Y, et al., 2017. Population synthesis based on joint distribution inference without disaggregate samples. J Artif Soc Soc Simul, 20(4):16. https://doi.org/10.18564/jasss.3533
Ye PJ, Wang S, Wang FY, 2018. A general cognitive architecture for agent-based modeling in artificial societies. IEEE Trans Comput Soc Syst, 5(1):176–185. https://doi.org/10.1109/TCSS.2017.2777602
Ye PJ, Zhu FH, Sabri S, et al., 2020. Consistent population synthesis with multi-social relationships based on tensor decomposition. IEEE Trans Intell Transp Syst, 21(5):2180–2189. https://doi.org/10.1109/TITS.2019.2916867
Ye PJ, Chen YY, Zhu FH, et al., 2021a. Bridging the micro and macro: calibration of agent-based model using mean-field dynamics. IEEE Trans Cybern, early access. https://doi.org/10.1109/TCYB.2021.3089712
Ye PJ, Wang X, Xiong G, et al., 2021b. TiDEC: a two-layered integrated decision cycle for population evolution. IEEE Trans Cybern, 51(12):5897–5906. https://doi.org/10.1109/TCYB.2019.2957574
Yun WS, Moon IC, Lee TE, 2015. Agent-based simulation of time to decide: military commands and time delays. J Artif Soc Soc Simul, 18(4):10. https://doi.org/10.18564/jasss.2871
Zhang C, Gao F, Jia BX, et al., 2019. RAVEN: a dataset for relational and analogical visual REasoNing. IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.5312–5322. https://doi.org/10.1109/CVPR.2019.00546
Zhang JJ, Wang FY, Wang X, et al., 2018. Cyber-physical-social systems: the state of the art and perspectives. IEEE Trans Comput Soc Syst, 5(3):829–840. https://doi.org/10.1109/TCSS.2018.2861224
Zheng WB, Yan L, Gou C, et al., 2020. Webly supervised knowledge embedding model for visual reasoning. IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.12442–12451. https://doi.org/10.1109/CVPR42600.2020.01246
Zheng WB, Yan L, Gou C, et al., 2021. KM4: visual reasoning via Knowledge embedding Memory Model with Mutual Modulation. Inform Fus, 67:14–28. https://doi.org/10.1016/j.inffus.2020.10.007
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Project supported by the National Natural Science Foundation of China (Nos. 62076237, 62073321, and U1811463) and the Youth Innovation Promotion Association, Chinese Academy of Sciences (No. 2021130)
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Fei-Yue WANG proposed the original idea for parallel cognition. Peijun YE designed the research and drafted this paper. Wenbo ZHENG helped design the experiments. Fei-Yue WANG, Xiao WANG, and Qinglai WEI helped organize the paper. Fei-Yue WANG revised and finalized the paper.
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Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, and Fei-Yue WANG declare that they have no conflict of interest.
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Ye, P., Wang, X., Zheng, W. et al. Parallel cognition: hybrid intelligence for human-machine interaction and management. Front Inform Technol Electron Eng 23, 1765–1779 (2022). https://doi.org/10.1631/FITEE.2100335
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DOI: https://doi.org/10.1631/FITEE.2100335