Abstract
Brain-machine integration is a new intelligent technology and system, which is a combination of natural intelligence and artificial intelligence. In order to make this integration effective and co-adaptive biological brain and machine should work collaboratively. A cognitive model of brain-machine integration will be proposed. Environment awareness and collaboration approaches will be explored in the paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shi, Z.: Mind Computation. World Scientific Publishing, Singapore (2017)
DiGiovanna, J., Mahmoudi, B., Fortes, J., et al.: Coadaptive brain-machine interface via reinforcement learning. IEEE Trans. Biomed. Eng. 56(1), 54–64 (2009)
Fukuyama, O., Suzuki, T., Mabuchi, K.: RatCar: a vehicular neuro-robotic platform for a rat with a sustaining structure of the rat body under the vehicle. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2010)
O’Doherty, J.E., Lebedev, M.A., Ifft, P.J., et al.: Active tactile exploration using a brain-machine-brain interface. Nature 479(7372), 228–231 (2011)
Wang, Y.M., Lu, M.L., Wu, Z., et al.: Ratbot: a rat “understanding” what humans see. In: International Workshop on Intelligence Science, in conjunction with IJCAI-2013, pp. 63–68 (2013)
Wu, Z., et al.: Cyborg intelligence: research progress and future directions. IEEE Intell. Syst. 31(6), 44–50 (2016)
Berger, T.W., et al.: A cortical neural prosthesis for restoring and enhancing memory. J. Neural Eng. 8(4) (2011). https://doi.org/10.1088/1741-2560/8/4/046017
Wu, Z., Pan, G., Zheng, N.: Cyborg intelligence. IEEE Intell. Syst. 28(5), 31–33 (2013)
Wu, Z., Zheng, N., Zhang, S., et al.: Maze Learning by hybrid brain-computer systems, Scientific Report, 9 (2016). https://doi.org/10.1038/srep31746
Endsley, M.R.: Toward a theory of situation awareness in dynamic systems. Hum. Factors 37(1), 32–64 (1995)
Shi, Z., Zhang, J., Yue, J., Yang, X.: A cognitive model for multi-agent collaboration. Int. J. Intell. Sci. 4(1), 1–6 (2014)
Gray, B.: Collaborating: Finding Common Ground for Multiparty Problems. Jossey-Bass, San Francisco (1989)
Shi, Z., Zhang, J., Yang, X., Ma, G., Qi, B., Yue, J.: Computational cognitive models for brain–machine collaborations. IEEE Intel. Syst. 29, 24–31 (2014)
Maslow, A.H.: Motivation and Personality. Addison-Wesley, Boston (1954, 1970, 1987)
Cohen, P.R., Levesque, H.J.: Intention is choice with commitment. Artif. Intell. 42(2–3), 213–361 (1990)
Jennings, N.R., Mamdani, E.H.: Using Joint Responsibility to Coordinate Collaborative Problem Solving in Dynamic Environments. AAAI1992, pp. 269–275 (1992)
Zhao, X., Tian, D., Chen, L., Shi, Z.: Reasoning theory for D3L with compositional bridge rules. In: Shi, Z., Leake, D., Vadera, S. (eds.) IIP 2012. IAICT, vol. 385, pp. 106–115. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32891-6_15
Acknowledgements
This work is supported by the National Program on Key Basic Research Project (973) (No. 2013CB329502), National Natural Science Foundation of China (No. 61035003).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Shi, Z., Huang, Z. (2019). Cognitive Model of Brain-Machine Integration. In: Hammer, P., Agrawal, P., Goertzel, B., Iklé, M. (eds) Artificial General Intelligence. AGI 2019. Lecture Notes in Computer Science(), vol 11654. Springer, Cham. https://doi.org/10.1007/978-3-030-27005-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-27005-6_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27004-9
Online ISBN: 978-3-030-27005-6
eBook Packages: Computer ScienceComputer Science (R0)