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Reward Potentials for Planning with Learned Neural Network Transition Models.

B. Say, S. Sanner, и S. Thiébaux. CP, том 11802 из Lecture Notes in Computer Science, стр. 674-689. Springer, (2019)

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Scalable Planning with Deep Neural Network Learned Transition Models.G. Wu, B. Say, и S. Sanner. J. Artif. Intell. Res., (2020)Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models.B. Say, и S. Sanner. IJCAI, стр. 4815-4821. ijcai.org, (2018)Scalable Nonlinear Planning with Deep Neural Network Learned Transition Models.G. Wu, B. Say, и S. Sanner. CoRR, (2019)Training Experimentally Robust and Interpretable Binarized Regression Models Using Mixed-Integer Programming.S. Tule, N. Le, и B. Say. CoRR, (2021)Rapid Identification of Protein Formulations with Bayesian Optimisation.V. Huynh, B. Say, P. Vogel, L. Cao, G. Webb, и A. Aleti. ICMLA, стр. 776-781. IEEE, (2023)Mathematical Programming Models for Optimizing Partial-Order Plan Flexibility.B. Say, A. Ciré, и J. Beck. ECAI, том 285 из Frontiers in Artificial Intelligence and Applications, стр. 1044-1052. IOS Press, (2016)Training Experimentally Robust and Interpretable Binarized Regression Models Using Mixed-Integer Programming.S. Tule, N. Le, и B. Say. SSCI, стр. 838-845. IEEE, (2022)Reward Potentials for Planning with Learned Neural Network Transition Models.B. Say, S. Sanner, и S. Thiébaux. CP, том 11802 из Lecture Notes in Computer Science, стр. 674-689. Springer, (2019)Compact and efficient encodings for planning in factored state and action spaces with learned Binarized Neural Network transition models.B. Say, и S. Sanner. Artif. Intell., (2020)A Unified Framework for Planning with Learned Neural Network Transition Models.B. Say. AAAI, стр. 5016-5024. AAAI Press, (2021)

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