Reinforcement learning for field development policy optimization
Giorgio de Paola, Cristina Ibanez-Llano, et al.
ATCE 2020
In domains such as homeland security, cybersecurity, and competitive marketing, it is frequently the case that analysts need to forecast actions by other intelligent agents that impact the problem of interest. Standard structured expert judgment elicitation techniques may fall short in this type of problem as they do not explicitly take into account intentionality. We present a decomposition technique based on adversarial risk analysis followed by a behavioural recomposition using discrete choice models that facilitate such elicitation process and illustrate its reasonable performance through behavioural experiments.
Giorgio de Paola, Cristina Ibanez-Llano, et al.
ATCE 2020
Debarun Bhattacharjya, Balaji Ganesan, et al.
NeurIPS 2024
Radu Marinescu, Haifeng Qian, et al.
NeurIPS 2022
Ademir Ferreira Da Silva, Levente Klein, et al.
INFORMS 2022