Reward-based Monte Carlo-Bayesian reinforcement learning for cyber preventive maintenance

Computers & Industrial Engineering - Tập 126 - Trang 578-594 - 2018
Theodore T. Allen1, Sayak Roychowdhury2, Enhao Liu1
1The Ohio State University, Integrated Systems Engineering, 1971 Neil Avenue – 210 Baker Systems, Columbus, OH 43221, United States
2Indian Institute of Technology, Kharagpur, Industrial and Systems Engineering, Kharagpur 721302, India

Tài liệu tham khảo

Alagoz, 2015, Optimally solving Markov decision processes with total expected discounted reward function: Linear programming revisited, Computers & Industrial Engineering, 87, 311, 10.1016/j.cie.2015.05.031

Afful-Dadzie, 2016, Control charting methods for autocorrelated cyber vulnerability data, Quality Engineering, 28, 313, 10.1080/08982112.2015.1125926

Afful-Dadzie, 2014, Data-driven cyber-vulnerability maintenance policies, Journal of Quality Technology, 46, 234, 10.1080/00224065.2014.11917967

Allen, 2011

Allen, 2017, Timely decision analysis enabled by efficient social media modeling, Decision Analysis, 14, 250, 10.1287/deca.2017.0360

Ando, 2005, A framework for learning predictive structures from multiple tasks and unlabeled data, Journal of Machine Learning Research, 6, 1817

Argyriou, 2008, Convex multi-task feature learning, Machine Learning, 73, 243, 10.1007/s10994-007-5040-8

Bakker, 2003, Task clustering and gating for bayesian multitask learning, Journal of Machine Learning Research, 4, 83

Bellman, 1957

Calandriello, D., Lazaric, A., & Restelli, M. (2014). Sparse multi-task reinforcement learning. In Advances in Neural Information Processing Systems (pp. 819–827).

Cheng, 2017, Joint optimization of lot sizing and condition-based maintenance for multi-component production systems, Computers & Industrial Engineering, 110, 538, 10.1016/j.cie.2017.06.033

Cockburn, 2009, Websites here, websites there, websites everywhere..., But are they secure?, The Quaestor Quarterly, 4, 1

Clarke, 2016, Conclusion: Key Themes for the Next President, The ANNALS of the American Academy of Political and Social Science, 668, 212, 10.1177/0002716216675825

Chen, 1997, Statistical applications of the Poisson-binomial and conditional Bernoulli distributions, Statistica Sinica, 875

Delage, 2010, Percentile optimization for Markov decision processes with parameter uncertainty, Operations Research, 58, 203, 10.1287/opre.1080.0685

Duff, 2002

Evgeniou, 2005, Learning multiple tasks with kernel methods, Journal of Machine Learning Research, 6, 615

Ghavamzadeh, 2015, Bayesian reinforcement learning: A survey. Foundations and Trends®, Machine Learning, 8, 359, 10.1561/2200000049

Harrell, 1982, A new distribution-free quantile estimator, Biometrika, 69, 635, 10.1093/biomet/69.3.635

Hou, 2015

Jafari, 2016, Optimal lot-sizing and maintenance policy for a partially observable production system, Computers & Industrial Engineering, 93, 88, 10.1016/j.cie.2015.12.009

Jalali, A., Sanghavi, S., Ruan, C., & Ravikumar, P. K. (2010). A dirty model for multi-task learning. In Advances in neural information processing systems (pp. 964–972).

Jawanpuria, P., & Nath, J. S., (2012). A convex feature learning formulation for latent task structure discovery In Proceedings of the 29th International Conference on Machine Learning, 2012

Kato, 2010, Conic programming for multitask learning, IEEE Transactions on Knowledge and Data Engineering, 22, 957, 10.1109/TKDE.2009.142

Lazaric, A., & Ghavamzadeh, M. (2010). Bayesian multi-task reinforcement learning. In ICML-27th International Conference on Machine Learning Omnipress (pp. 599–606).

Maurer, A., Pontil, M., & Romera-Paredes, B. (2013, February). Sparse coding for multitask and transfer learning. In International Conference on Machine Learning (pp. 343–351).

McKay, 1979, Comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 21, 239

Misra, I., Shrivastava, A., Gupta, A., & Hebert, M. (2016). Cross-stitch networks for multi-task learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3994–4003).

Parisotto E., Ba J., & Salakhutdinov, R. (2016). Actor-mimic: Deep multitask and transfer reinforcement learning. In Proceedings of the 4th International Conference on Learning Representations.

Pineau, 2003, Point-based value iteration: An anytime algorithm for POMDPs, In IJCAI, 3, 1025

Ponemon Institute (2017). cost of cybercrime study: United States. (2017). https://www.accenture.com/us-en/insight-cost-of-cybercrime-2017.

Poupart, 2006, An analytic solution to discrete Bayesian reinforcement learning, Proceedings of the 23rd International Conference on Machine Learning, 697, 10.1145/1143844.1143932

Puterman, 2014

Ray, 2010, Model-based reinforcement learning, Encyclopedia of Machine Learning, 690

Roychowdhury, S. (2017). Data-Driven Policies for Manufacturing Systems and Cyber Vulnerability Maintenance (Doctoral dissertation, The Ohio State University).

Ross, 2011, A Bayesian approach for learning and planning in partially observable Markov decision processes, Journal of Machine Learning Research, 12, 1729

Shani, 2006, Prioritizing point-based POMDP solvers, 389

Smallwood, 1973, The optimal control of partially observable Markov processes over a finite horizon, Operations Research, 21, 1071, 10.1287/opre.21.5.1071

Spaan, 2005, PERSEUS: Randomized point-based value iteration for POMDPs, Journal of Artificial Intelligence Research, 24, 195, 10.1613/jair.1659

Srinivasan, 2013, Value of condition monitoring in infrastructure maintenance, Computers & Industrial Engineering, 66, 233, 10.1016/j.cie.2013.05.022

Sutton, 1998, Vol. 135

Tang, 1993, Orthogonal array-based Latin hypercubes, Journal of the American Statistical Association, 88, 1392, 10.1080/01621459.1993.10476423

Taylor, 2009, Transfer learning for reinforcement learning domains: A survey, Journal of Machine Learning Research, 10, 1633

Walraven, E. (2017), https://github.com/AlgTUDelft/SolvePOMDP (accessed 8-24-2018).

Wang, Y., Won, K. S., Hsu, D. and Lee, W. S. (2012). Monte Carlo Bayesian Reinforcement Learning. arXiv preprint arXiv:1206.6449.

Wiering, M., & Van Otterlo, M. (2012). Reinforcement learning. Adaptation, Learning, and Optimization, 12.

Wilson, 2007, Multi-task reinforcement learning: a hierarchical Bayesian approach, Proceedings of the 24th international conference on Machine learning, 1015, 10.1145/1273496.1273624

Zhang, Y., & Yang, Q. (2017). A survey on multi-task learning. arXiv preprint arXiv:1707.08114.

Thông tin
Thông tin xuất bản

Computers & Industrial Engineering

Tập 126

578-594

Thông tin tác giả