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Monte Carlo Tree Search (MCTS) is a pronounced empirical search algorithm for agent decision-making, especially when enhanced by Deep Learning (DL), in mastering board games that were once thought to be unconquerable. However, it does not appear to be as equally successful in the domain of real-time video games, where the simulation time limit for exploration is a crucial factor, since they are generally designed to be played by human users and hence require a significant amount of resources for simulation. We in this paper propose a surrogate-assisted MCTS approach, specifically targeting commercial real-time video games by approximating the result of gameplay with a deep-learning-based surrogate model. The key contribution of our work is that we designed a modified MCTS for video games that are both commercial and processed in real-time. Since commercial video games include considerably more complex and dynamic gameplays to satisfy their market consumers, as opposed to their non-commercial analogs, our work can be regarded as having challenged the domain unattempted by precedent studies. We validated the performance of our method by conducting a comparative experiment with other algorithms, including the traditional MCTS, under the environment of a commercial real-time video game. [Display omitted] • A Monte Carlo tree search (MCTS) method applicable to commercial real-time video games is proposed. • The method integrates MCTS with a deep neural network structure to function efficiently in a real-time environment. • Performance verification of the proposed surrogate-assisted MCTS is done via two commercial real-time video games with distinct features. • Our surrogate model can be trained with a comparatively little amount of computational resources in real-time video games. [ABSTRACT FROM AUTHOR]

Soil water retention curves (SWRC) are constitutive relationships that are critical for predicting the hydro-mechanical behaviour of unsaturated soil in a wide range of applications in geoenvironmental and geotechnical engineering, hydrology, and agricultural and soil science. Directly measuring SWRC can be costly and time-consuming. Recent research has shown that machine learning, especially artificial neural networks (ANNs), can effectively predict SWRC using easy-to-measure soil properties as inputs. However, significant questions remain about prediction accuracy and what are the most effective input parameters to use, and only shallow neural networks with one hidden layer have been developed in the past for this purpose. In this study, effectiveness of deep neural networks (DNNs) - ANNs with more than one hidden layer - for SWRC prediction is explored for a range of models with different combinations that require easy-to-measure input parameters. Specifically, the study investigates whether switching from a shallow neural network (SNN) to a deep neural network (DNN) can enhance the accuracy of SWRC predictions for these models. Significant differences are found in seven out of thirteen cases when shifting from SNN to DNN. It was also found that models including soil texture, augmented by either dry density or soil porosity as input parameters, perform well. Specific gravity, soil depth, and organic content, on the other hand, do not add to the predictive capability of these models. Findings of this paper can help improve SWRC prediction using machine learning and can lead to more accurate modelling of unsaturated soil behaviour. [ABSTRACT FROM AUTHOR]

Monte Carlo Tree Search (MCTS) is a pronounced empirical search algorithm for agent decision-making, especially when enhanced by Deep Learning (DL), in mastering board games that were once thought to be unconquerable. However, it does not appear to be as equally successful in the domain of real-time video games, where the simulation time limit for exploration is a crucial factor, since they are generally designed to be played by human users and hence require a significant amount of resources for simulation. We in this paper propose a surrogate-assisted MCTS approach, specifically targeting commercial real-time video games by approximating the result of gameplay with a deep-learning-based surrogate model. The key contribution of our work is that we designed a modified MCTS for video games that are both commercial and processed in real-time. Since commercial video games include considerably more complex and dynamic gameplays to satisfy their market consumers, as opposed to their non-commercial analogs, our work can be regarded as having challenged the domain unattempted by precedent studies. We validated the performance of our method by conducting a comparative experiment with other algorithms, including the traditional MCTS, under the environment of a commercial real-time video game. [Display omitted] • A Monte Carlo tree search (MCTS) method applicable to commercial real-time video games is proposed. • The method integrates MCTS with a deep neural network structure to function efficiently in a real-time environment. • Performance verification of the proposed surrogate-assisted MCTS is done via two commercial real-time video games with distinct features. • Our surrogate model can be trained with a comparatively little amount of computational resources in real-time video games. [ABSTRACT FROM AUTHOR]

Soil water retention curves (SWRC) are constitutive relationships that are critical for predicting the hydro-mechanical behaviour of unsaturated soil in a wide range of applications in geoenvironmental and geotechnical engineering, hydrology, and agricultural and soil science. Directly measuring SWRC can be costly and time-consuming. Recent research has shown that machine learning, especially artificial neural networks (ANNs), can effectively predict SWRC using easy-to-measure soil properties as inputs. However, significant questions remain about prediction accuracy and what are the most effective input parameters to use, and only shallow neural networks with one hidden layer have been developed in the past for this purpose. In this study, effectiveness of deep neural networks (DNNs) - ANNs with more than one hidden layer - for SWRC prediction is explored for a range of models with different combinations that require easy-to-measure input parameters. Specifically, the study investigates whether switching from a shallow neural network (SNN) to a deep neural network (DNN) can enhance the accuracy of SWRC predictions for these models. Significant differences are found in seven out of thirteen cases when shifting from SNN to DNN. It was also found that models including soil texture, augmented by either dry density or soil porosity as input parameters, perform well. Specific gravity, soil depth, and organic content, on the other hand, do not add to the predictive capability of these models. Findings of this paper can help improve SWRC prediction using machine learning and can lead to more accurate modelling of unsaturated soil behaviour. [ABSTRACT FROM AUTHOR]