The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Where does AlphaGo go: from church-turing thesis to AlphaGo thesis and beyond
Abstract
An investigation on the impact and significance of the AlphaGo vs. Lee Sedol Go match is conducted, and concludes with a conjecture of the AlphaGo Thesis and its extension in accordance with the Church-Turing Thesis in the history of computing. It is postulated that the architecture and method utilized by the AlphaGo program provide an engineering solution for tackling issues in complexity and intelligence. Specifically, the AlphaGo Thesis implies that any effective procedure for hard decision problems such as NP-hard can be implemented with AlphaGo-like approach. Deep rule-based networks are proposed in attempt to establish an understandable structure for deep neural networks in deep learning. The success of AlphaGo and corresponding thesis ensure the technical soundness of the parallel intelligence approach for intelligent control and management of complex systems and knowledge automation.
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Cites
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
The complexity of theorem-proving procedures
Cook, S. A. (1971) · Proceedings of the third annual ACM symposium on Theory of computing - STOC '71 · doi:10.1145/800157.805047 · Cited by 6,385
Cited by
Fan, C., Zhang, C., Yahja, A., & Mostafavi, A. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.102049 · Cited by 434
An Overview of Smart Contract: Architecture, Applications, and Future Trends
Wang, S., Yuan, Y., Wang, X., Li, J., Qin, R., & Wang, F.-Y. (2018) · 2018 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2018.8500488 · Cited by 398
Self-citationParallel Transportation Systems: Toward IoT-Enabled Smart Urban Traffic Control and Management
Zhu, F., Lv, Y., Chen, Y., Wang, X., Xiong, G., & Wang, F.-Y. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2934991 · Cited by 375
Self-citationArtificial Intelligence Techniques in Smart Grid: A Survey
Omitaomu, O. A., & Niu, H. (2021) · Smart Cities · doi:10.3390/smartcities4020029 · Cited by 371
Monte Carlo Tree Search: a review of recent modifications and applications
Świechowski, M., Godlewski, K., Sawicki, B., & Mańdziuk, J. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10228-y · Cited by 365
Metadata
- Authors
- Wang, F.-Y., Zhang, J. J., Zheng, X., Wang, X., Yuan, Y., Dai, X., Zhang, J., & Yang, L.
- Venue
- IEEE/CAA Journal of Automatica Sinica
- Published
- 2016-04
- Volume
- 3
- Issue
- 2
- Pages
- 113-120
- ISSN
- 2329-9266 (ISSN-L)
- 2329-9274
- OpenAlex
- W2404399993
- Cited by
- 342
- References
- 2
- FWCI
- 23.79
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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