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Agricultural Library and Information ›› 2019, Vol. 31 ›› Issue (4): 4-18.doi: 10.13998/j.cnki.issn1002-1248.2019.04.19-0150

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• Special review •     Next Articles

Cognitive Computing and Applications in Agriculture

WANG Ting1,2, CUI Yunpeng1,2, WANG Jian1,2, LIU Tingting1,2, WANG Mo1,2   

  1. 1.Information Institute of Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
    2.Key Laboratory of Big Agri-data, Ministry of agriculture and rural areas, Beijing 100081, China
  • Received:2019-03-06 Online:2019-04-05 Published:2019-06-21

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Abstract: Cognitive computing is a nascent interdisciplinary domain, and it is also an evolution of technology that attempts to make sense of a complex world that is drowning in data in all forms and shapes. It is a confluence of cognitive science, neuroscience, date science, and cloud computing, which makes cognitive computing powerful and has the potential for groundbreaking discoveries and advances. We are entering a new era in cognitive computing that will transform the way humans collaborate with machines to gain actionable insights in areas such as healthcare, manufacturing, transportation, retail, retail, and financial services. Served as a catalyst for advancing research in cognitive computing, a coherent body of knowledge and recent research in cognitive computing are brought together. First, a deep look was taken at the concept of cognitive computing and an interdisciplinary introduction to cognitive computing, which was to provide a unified view of the discipline. Second, the development procedure was provided. Thirdly, overview of three major categories of cognitive architectures and principal technologies and approaches that are fundamental to a cognitive system were demonstrated. Some of the industries that were early adopters of cognitive com-puting and the types of solutions that were being created were also included. Finally, the applications of cognitive computing in agricultural area was discussed. It covered the applications, the system, the future and its challenges. Cognitive systems can help with the transfer of knowledge and best practices in agricultural area, and using cognitive computing to help decision support services has huge potential. In these use cases, a cognitive system is designed to build a dialog between human and machine so that best practices are learned by the system as opposed to being programmed as a set of rules. It is clear that cognitive computing is in its early stages of maturation. The list of potential uses of a cognitive computing approach will continue to grow over time, and the coming decade will bring many new software and hardware innovations to stretch the limits of what is possible.

Key words: cognitive computing, cognitive system, agricultural cognitive system, artificial intelligence

CLC Number: 

  • G202
[1] Marg E.DESCARTES' ERROR: emotion, reason, and the human brain[J]. Optometry and Vision Science. 1995, 72(11): 847-848.
[2] Preissl, R., et al.Compass: A scalable simulator for an architecture for cognitive computing[C]. in Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society Press. 2012:1-11.
[3] Wang Y.Towards the synergy of cognitive informatics, neural informatics, brain informatics, and cognitive computing[J]. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2011,5(1):75-93.
[4] Modha, D.S., et al.Cognitive computing[J]. Communications of the ACM. 2011,54(8): 62-71.
[5] Nahamoo D.Cognitive computing journey[C]. in Proceedings of the first workshop on Parallel programming for analytics applications. ACM. 2014.
[6] Li, J., et al.Concept learning via granular computing: a cognitive viewpoint[J]. Information Sciences. 2015, (298): 447-467.
[7] Raghavan, V.V., et al., Cognitive computing: Theory and applications[M]. Elsevier. 2016, 35.
[8] Banavar, G. and M. Cooper. Turing Lecture 2017 Cognitive Computing[J]. ITNOW. 2016, 58(4): 62-63.
[9] Reynolds, H. and S. Feldman. Cognitive computing: Beyond the hype[J]. KM World. 2014, 27.
[10] Frankish, K. and W. Ramsey. The Cambridge handbook of cognitive science[M]. Cambridge University Press.. 2012.
[11] Friedenberg, J. and G. Silverman. Cognitive science: An introduction to the study of mind[M]. Sage. 2011.
[12] Hurwitz J.S.,M. Kaufman, and A. Bowles. Cognitive computing and big data analytics[M]. John Wiley & Sons. 2015.
[13] Gupta, S., et al.Big data with cognitive computing: a review for the future[J]. International Journal of Information Management. 2018, 42:78-89.
[14] Langley P.,J.E. Laird, and S. Rogers. Cognitive architectures: Research issues and challenges[J]. Cognitive Systems Research. 2009, 10(2): 141-160.
[15] Anderson J.R.The architecture of cognition[M]. Psychology Press.. 2013.
[16] Anderson J.R.ACT: A simple theory of complex cognition[J]. American psychologist. 1996, 51(4): 355.
[17] Lehman J.F.,J.E. Laird, and P. Rosenbloom. A gentle introduction to Soar, an architecture for human cognition[J]. Invitation to cognitive science. 1996, 4: 212-249.
[18] Lehman J.F.,J. Laird, and P. Rosenbloom. A gentle introduction to soar, an architecture for human cognition: 2006 update[J]. University of Michigan. 2006, 1-37.
[19] Card S.K.The psychology of human-computer interaction[M]. CRC Press.. 2018.
[20] Flusberg, S.J. and J.L. McClelland. Connectionism and the emergence of mind[M]. The Oxford Handbook of Cognitive Science. 2014, 10.
[21] Goldberg Y.A primer on neural network models for natural language processing[J]. Journal of Artificial Intelligence Research. 2016, 57:345-420.
[22] Mundy, A., et al.An efficient SpiNNaker implementation of the neural engineering framework[C]. in 2015 International Joint Conference on Neural Networks (IJCNN). IEEE. 2015.
[23] Eliasmith, C., et al.A large-scale model of the functioning brain[J]. Science. 2012, 338(6111): 1202-1205.
[24] Eliasmith C.How to build a brain: A neural architecture for biological cognition [M]. Oxford University Press.. 2013.
[25] Balduzzi D.Semantics, Representations and Grammars for Deep Learning[J]. Computer Science, 2015.
[26] George, D. and J. Hawkins. A hierarchical Bayesian model of invariant pattern recognition in the visual cortex[C]. in Proceedings. 2005 IEEE International Joint Conference on Neural Networks. IEEE. 2005.
[27] Snaider J.,R. McCall, and S. Franklin. The LIDA framework as a general tool for AGI[C]. in International Conference on Artificial General Intelligence. Springer. 2011.
[28] Franklin, S., et al.LIDA: A systems-level architecture for cognition, emotion, and learning[J]. IEEE Transactions on Autonomous Mental Development. 2014, 6(1): 19-41.
[29] Rosenbloom P.S.The Sigma cognitive architecture and system[J]. AISB Quarterly. 2013, 136:4-13.
[30] Porreca, S., et al.Accessing Government Open Data Through Chatbots[C]. in International Conference on Web Engineering. Springer. 2017.
[31] Ferrucci, D., et al.Building Watson: An overview of the DeepQA project[J]. AI magazine. 2010, 31(3):59-79.
[32] Rice K.L.,T.M. Taha, and C.N. Vutsinas. Scaling analysis of a neocortex inspired cognitive model on the Cray XD1[J]. The Journal of Supercomputing. 2009, 47(1):21-43.
[33] Chang A, Monroe W, Savva M, et al.Text to 3D Scene Generation with Rich Lexical Grounding[J]. Computer Science, 2015.
[34] Kelly III, J.E. and S. Hamm. Smart machines: IBM's Watson and the era of cognitive computing[M]. Columbia University Press.. 2013.
[35] Demchenko Y.,C. De Laat, and P. Membrey. Defining architecture components of the Big Data Ecosystem[C]. in 2014 International Conference on Collaboration Technologies and Systems (CTS). IEEE. 2014.
[36] LeCun, Y., Y. Bengio, and G. Hinton. Deep learning[J]. Nature. 2015, 521(7553):436.
[37] Tian, Y., et al.A formal knowledge representation system (FKRS) for the intelligent knowledge base of a cognitive learning engine[J]. International Journal of Software Science and Computational Intelligence (IJSSCI). 2011, 3(4): 1-17.
[38] Wang Y.On concept algebra: A denotational mathematical structure for knowledge and software modeling[J]. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI). 2008, 2(2):1-19.
[39] Wang Y.The real-time process algebra (RTPA)[J]. Annals of Software Engineering. 2002, 14(1-4): 235-274.
[40] ElBedwehy, M.N., et al. A computational knowledge representation model for cognitive computers[J]. Neural Computing and Applications. 2014, 25(7-8):1517-1534.
[41] Xiao, T. and S. Lan. Knowledge Reduction based on Cognitive Model of Granular Computing[J]. Advances in Information Sciences and Service Sciences. 2013, 5(6):733.
[42] Pena-Ayala, A. and R. Mizoguchi. Intelligent decision-making approach based on fuzzy-causal knowledge and reasoning[C]. in International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer. 2012.
[43] Heckerman, D. and R. Shachter. Decision-theoretic foundations for causal reasoning[J]. Journal of Artificial Intelligence Research. 1995, 3:405-430.
[44] Zadeh L A .Fuzzy logic = computing with words[M]. Computing with Words in Information/Intelligent Systems 1. Physica-Verlag HD, 1999.
[45] Eden C.On the nature of cognitive maps[J]. Journal of management studies. 1992, 29(3):261-265.
[46] Wang Y.On denotational mathematics foundations for the next generation of computers: Cognitive computers for knowledge processing[J]. Journal of Advanced Mathematics and Applications. 2012, 1(1):121-133.
[47] Wang Y.Inference algebra (IA): A denotational mathematics for cognitive computing and machine reasoning (I)[J]. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI). 2011, 5(4):61-82.
[48] Arthur, J.V., et al.Building block of a programmable neuromorphic substrate: A digital neurosynaptic core[C]. in the 2012 international joint conference on Neural networks (IJCNN). IEEE. 2012.
[49] Imam, N., et al.A digital neurosynaptic core using event-driven qdi circuits[C]. in 2012 IEEE 18th International Symposium on Asynchronous Circuits and Systems. IEEE. 2012.
[50] Merolla, P., et al.A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm[C]. in 2011 IEEE custom integrated circuits conference (CICC). IEEE. 2011.
[51] Esser, S.K., et al.Cognitive computing systems: Algorithms and applications for networks of neurosynaptic cores[C]. in The 2013 International Joint Conference on Neural Networks (IJCNN). IEEE. 2013.
[52] Yelick, K., et al.Productivity and performance using partitioned global address space languages[C]. in Proceedings of the 2007 international workshop on Parallel symbolic computation. ACM. 2007.
[53] Merolla, P.A., et al.A million spiking-neuron integrated circuit with a scalable communication network and interface[J]. Science. 2014, 345(6197):668-673.
[54] Amir, A., et al.Cognitive computing programming paradigm: a corelet language for composing networks of neurosynaptic cores[C]. in The 2013 International Joint Conference on Neural Networks (IJCNN). IEEE. 2013.
[55] Djurfeldt M.The connection-set algebra—a novel formalism for the representation of connectivity structure in neuronal network models[J]. Neuroinformatics. 2012, 10(3):287-304.
[56] Djurfeldt M.The Connection-set Algebra: a formalism for the representation of connectivity structure in neuronal network models, implementations in Python and C++, and their use in simulators[J]. BMC neuroscience. 2011, 12(1):80.
[57] Lawniczak, A.T. and B.N. Di Stefano, Computational intelligence based architecture for cognitive agents[J]. Procedia Computer Science. 2010, 1(1):2227-2235.
[58] ANDERSON, I.R. and B.I. REISER, THE LISP TUTGR[M]. 1985.
[59] Koedinger, K.R., et al.Intelligent tutoring goes to school in the big city[J]. International Journal of Artificial Intelligence in Education (IJAIED). 1997, 8:30-43.
[60] Seo, M., et al.Solving geometry problems: Combining text and diagram interpretation[C]. in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015.
[61] Matsuda, N., et al.Cognitive anatomy of tutor learning: Lessons learned with SimStudent[J]. Journal of Educational Psychology. 2013, 105(4):1152.
[62] Zadrozny W.W.,V. de Paiva, and L.S. Moss. Explaining watson: polymath style[C]. in Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015.
[63] Martinez P.,H. Bakardjian, and A. Cichocki. Multi-command real-time brain machine interface using SSVEP: feasibility study for occipital and forehead sensor locations[C]. in Advances in Cognitive Neurodynamics ICCN 2007. 2008, 783-786.
[64] Negueruela, C., et al.Brain-computer interfaces for space applications[J]. Personal and Ubiquitous Computing. 2011, 15(5):527-537.
[65] Hua X.-S.,M. Ye, and J. Li. Mining knowledge from clicks: MSR-Bing image retrieval challenge[C]. in 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW). IEEE. 2014.
[66] Bai, Y., et al.Bag-of-words based deep neural network for image retrieval[C]. in Proceedings of the 22nd ACM international conference on Multimedia. ACM. 2014.
[67] Dong J.,X. Li, and C.G. Snoek,Word2VisualVec: Cross-media retrieval by visual feature prediction[J]. arXiv preprint arXiv:1604.06838, 2016.
[68] Orozco H.,D. Thalmann, and F. Ramos. Making empathetic virtual humans in human-computer interaction scenarios[C]. in Proc. of the 11th Computer Graphics International. 2010.
[69] Costa, P.T. and R.R. McCrae. Normal personality assessment in clinical practice: The NEO Personality Inventory[J]. Psychological assessment. 1992, 4(1):5.
[70] Ekman P.,W.V. Friesen, and P. Ellsworth. Emotion in the human face: Guidelines for research and an integration of findings[M]. Elsevier. 2013, 11.
[71] Wang Y.Cognitive informatics: towards future generation computers that think and feel[C]. in 2006 5th IEEE International Conference on Cognitive Informatics. IEEE. 2006.
[72] Cambria, E., et al.Sentic blending: Scalable multimodal fusion for the continuous interpretation of semantics and sentics[C]. in 2013 IEEE symposium on computational intelligence for human-like intelligence (CIHLI). IEEE. 2013.
[73] Agarwal, A., et al.Cognitive compliance for financial regulations[J]. IT Professional. 2017, 19(4):28-35.
[74] Tarafdar M.,C. Beath, and J. Ross. Enterprise cognitive computing applications: Opportunities and challenges[J]. IT Professional. 2017.
[75] 章璐杰. 基于物联网的智慧葡萄园管理系统的优化研究[D].浙江大学,2017.
[76] 濮永仙. 物联网智能农业系统在瓜果生产中的应用研究[J]. 科技广场. 2016(1):92-97.
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