[1]JIANG Yinjie,KUANG Kun,WU Fei.Big data intelligence: from the optimal solution of data fitting to the equilibrium solution of game theory[J].CAAI Transactions on Intelligent Systems,2020,15(1):175-182.[doi:10.11992/tis.201911007]
Copy
Big data intelligence: from the optimal solution of data fitting to the equilibrium solution of game theory
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 1
Page number:
175-182
Column:
人工智能院长论坛
Public date:
2020-01-05
- Title:
- Big data intelligence: from the optimal solution of data fitting to the equilibrium solution of game theory
- Keywords:
- artificial intelligence; big data; optimal fitting; neural network architecture search; game theory; Nash equilibrium
- CLC:
- TP391
- DOI:
- 10.11992/tis.201911007
- Abstract:
- Data-driven machine learning (especially deep learning), which is a hot topic in artificial intelligence research, has made great progress in the fields of natural language processing, computer vision analysis and speech recognition, etc. The optimization of parameters in traditional machine learning can be regarded as the process of data fitting, the optimal model on the training data set is fitted by various optimization algorithms. However, in real applications such as commodity bidding and resource allocation, the target of artificial intelligence algorithm is not an optimal solution, but an equilibrium solution, which requires the application of the game theory to big data intelligence. Combining game theory with artificial intelligence can expand the application space of big data intelligence.
- References:
-
[1] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Red Hook, USA, 2012: 1097–1105.
[2] DENG Jia, DONG Wei, SOCHER R, et al. ImageNet: a large-scale hierarchical image database[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009: 248–255.
[3] YUILLE A L, LIU Chenxi. Deep nets: what have they ever done for vision?[J]. arXiv: 1805.04025, 2018.
[4] MOOSAVI-DEZFOOLI S M, FAWZI A, FAWZI O, et al. Universal adversarial perturbations[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017: 86–94.
[5] SILVER D, HUANG A, MADDISON C J, et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature, 2016, 529(7587): 484–489.
[6] BROWN N, SANDHOLM T. Superhuman AI for heads-up no-limit poker: libratus beats top professionals[J]. Science, 2018, 359(6374): 418–424.
[7] BLAIR A, SAFFIDINE A. AI surpasses humans at six-player poker[J]. Science, 2019, 365(6456): 864–865.
[8] ARULKUMARAN K, CULLY A, TOGELIUS J. Alphastar: an evolutionary computation perspective[J]. arXiv: 1902.01724, 2019.
[9] ROSENBLATT F. Principles of neurodynamics: perceptrons and the theory of brain mechanisms[R]. Washington: Spartan, 1961.
[10] WERBOS P. New tools for prediction and analysis in the behavioral sciences[D]. Cambridge: Harvard University, 1974.
[11] WERBOS P J. Backpropagation through time: what it does and how to do it[J]. Proceedings of the IEEE, 1990, 78(10): 1550–1560.
[12] RUMELHART D E, HINTON G E, WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533–536.
[13] CORTES C, VAPNIK V. Support-vector networks[J]. Machine learning, 1995, 20(3): 273–297.
[14] FREUND Y, SCHAPIRE R E. Experiments with a new boosting algorithm[C]//Proceedings of the 13th International Conference on Machine Learning. Bari, Italy, 1996: 148–156.
[15] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504–507.
[16] ROBBINS H, MONRO S. A stochastic approximation method[J]. The annals of mathematical statistics, 1951, 22(3): 400–407.
[17] DEVLIN J, CHANG Mingwei, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[J]. arXiv: 1810.04805, 2018.
[18] ZOPH B, LE Q V. Neural architecture search with reinforcement learning[C]//Proceedings of 5th International Conference on Learning Representations. Toulon, France, 2017.
[19] BAKER B, GUPTA O, NAIK N, et al. Designing neural network architectures using reinforcement learning[C]//Proceedings of International Conference on Learning Representations. Toulon, France, 2017.
[20] CAI Han, CHEN Tianyao, ZHANG Weinan, et al. Efficient architecture search by network transformation[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence. New Orleans, USA, 2018.
[21] SUGANUMA M, SHIRAKAWA S, NAGAO T. A genetic programming approach to designing convolutional neural network architectures[C]//Proceedings of Genetic and Evolutionary Computation Conference. Berlin, Germany, 2017: 497–504.
[22] CAI Han, YANG Jiacheng, ZHANG Weinan, et al. Path-level network transformation for efficient architecture search[C]//Proceedings of the 35th International Conference on Machine Learning. Stockholmsm?ssan, Stockholm, Sweden, 2018: 677–686.
[23] ELSKEN T, METZEN J H, HUTTER F. Efficient multi-objective neural architecture search via lamarckian evolution[C]//Proceedings of 2019 International Conference on Learning Representations. New Orleans, USA, 2019.
[24] ZOPH B, VASUDEVAN V, SHLENS J, et al. Learning transferable architectures for scalable image recognition[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018: 8697–8710.
[25] REAL E, AGGARWAL A, HUANG Yanping, et al. Regularized evolution for image classifier architecture search[J]. AAAI technical track: machine learning, 2019, 33(1): 4780–4789.
[26] VON NEUMANN J, MORGENSTERN O, KUHN H W, et al. Theory of games and economic behavior[M]. Princeton: Princeton University Press, 2007.
[27] NASH JR J F. Equilibrium points in n-person games[J]. Proceedings of the national academy of sciences of the United States of America, 1950, 36(1): 48–49.
[28] TANG Pingzhong. Reinforcement mechanism design[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. Melbourne, Australia, 2017: 5146–5150.
[29] PéROLAT J, LEIBO J Z, ZAMBALDI V, et al. A multi-agent reinforcement learning model of common-pool resource appropriation[C]//Proceedings of the 31st Conference on Neural Information Processing Systems. Long Beach, USA, 2017: 3643–3652.
[30] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge, USA, 2014: 2672–2680.
[31] SUTTON R S, MCALLESTER D, SINGH S, et al. Policy gradient methods for reinforcement learning with function approximation[C]//Proceedings of the 12th International Conference on Neural Information Processing Systems. Cambridge, USA, 1999: 1057–1063.
[32] KOCSIS L, SZEPESVáRI C. Bandit based monte-carlo planning[C]//Proceedings of the 17th European Conference on Machine Learning. Berlin, Germany, 2006: 282–293.
[33] SANDHOLM T. Solving imperfect-information games[J]. Science, 2015, 347(6218): 122–123.
[34] RACANIèRE S, WEBER T, REICHERT D P, et al. Imagination-augmented agents for deep reinforcement learning[C]//Proceedings of the 31st Conference on Neural Information Processing Systems. Long Beach, USA, 2017: 5690–5701.
[35] ZINKEVICH M, JOHANSON M, BOWLING M, et al. Regret minimization in games with incomplete information[C]//Proceedings of the 20th International Conference on Neural Information Processing Systems. Red Hook, USA, 2007: 1729–1736.
[36] BROWN N, SANDHOLM T. Safe and nested subgame solving for imperfect-information games[C]//Proceedings of the 31st Conference on Neural Information Processing Systems. Long Beach, USA, 2017: 689–699.
[37] VINYALS O, BABUSCHKIN I, CZARNECKI W M, et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning[J]. Nature, 2019, 575(7782): 350–354.
- Similar References:
Memo
-
Last Update:
1900-01-01