Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead

In this paper we propose a novel supervised learning approach for training Artificial Neural Networks (ANNs) to evaluate chess positions. The method that we present aims to train different ANN architectures to understand chess positions similarly to how highly rated human players do. We investigate the capabilities that ANNs have when it comes to pattern recognition, an ability that distinguishes chess grandmasters from more amateur players. We collect around 3,000,000 different chess positions played by highly skilled chess players and label them with the evaluation function of Stockfish, one of the strongest existing chess engines. We create 4 different datasets from scratch that are used for different classification and regression experiments. The results show how relatively simple Multilayer Perceptrons (MLPs) outperform Convolutional Neural Networks (CNNs) in all the experiments that we have performed. We also investigate two different board representations, the first one representing if a piece is present on the board or not, and the second one in which we assign a numerical value to the piece according to its strength. Our results show how the latter input representation influences the performances of the ANNs negatively in almost all experiments

Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead

In this paper we propose a novel supervised learning approach for training Artificial Neural Networks (ANNs) to evaluate chess positions. The method that we present aims to train different ANN architectures to understand chess positions similarly to how highly rated human players do. We investigate the capabilities that ANNs have when it comes to pattern recognition, an ability that distinguishes chess grandmasters from more amateur players. We collect around 3,000,000 different chess positions played by highly skilled chess players and label them with the evaluation function of Stockfish, one of the strongest existing chess engines. We create 4 different datasets from scratch that are used for different classification and regression experiments. The results show how relatively simple Multilayer Perceptrons (MLPs) outperform Convolutional Neural Networks (CNNs) in all the experiments that we have performed. We also investigate two different board representations, the first one representing if a piece is present on the board or not, and the second one in which we assign a numerical value to the piece according to its strength. Our results show how the latter input representation influences the performances of the ANNs negatively in almost all experiments

Two-stage visual navigation by deep neural networks and multi-goal reinforcement learning

In this paper, we propose a two-stage learning framework for visual navigation in which the experience of the agent during exploration of one goal is shared to learn to navigate to other goals. We train a deep neural network for estimating the robot's position in the environment using ground truth information provided by a classical localization and mapping approach. The second simpler multi-goal Q-function learns to traverse the environment by using the provided discretized map. Transfer learning is applied to the multi-goal Q-function from a maze structure to a 2D simulator and is finally deployed in a 3D simulator where the robot uses the estimated locations from the position estimator deep network. In the experiments, we first compare different architectures to select the best deep network for location estimation, and then compare the effects of the multi-goal reinforcement learning method to traditional reinforcement learning. The results show a significant improvement when multi-goal reinforcement learning is used. Furthermore, the results of the location estimator show that a deep network can learn and generalize in different environments using camera images with high accuracy in both position and orientation

Dynamic Parameter Update for Robot Navigation Systems through Unsupervised Environmental Situational Analysis

A robot’s local navigation is often done through forward simulation of robot velocities and measuring the possible trajectories against safety, distance to the final goal and the generated path of a global path planner. Then, the computed velocities vector for the winning trajectory is executed on the robot. This process is done continuously through the whole navigation process and requires an extensive amount of processing. This only allows for a very limited sampling space. In this paper, we propose a novel approach to automatically detect the type of surrounding environment based on navigation complexity using unsupervised clustering, and limit the local controller’s sampling space. The experimental results in 3D simulation and using a real mobile robot show that we can increase the navigation performance by at least thirty percent while reducing the number of failures due to collision or lack of sampling

A Deep Convolutional Neural Network for Location Recognition and Geometry based Information

In this paper we propose a new approach to Deep Neural Networks (DNNs) based on the particular needs of navigation tasks. To investigate these needs we created a labeled image dataset of a test environment and we compare classical computer vision approaches with the state of the art in image classification. Based on these results we have developed a new DNN architecture that outperforms previous architectures in recognizing locations, relying on the geometrical features of the images. In particular we show the negative effects of scale, rotation, and position invariance properties of the current state of the art DNNs on the task. We finally show the results of our proposed architecture that preserves the geometrical properties. Our experiments show that our method outperforms the state of the art image classification networks in recognizing locations

Boon and burden: economic performance and future perspectives of the Venice flood protection system

Sea-level rise (SLR) and flooding are among the climate change stressors challenging human society in the twenty-first century. Many coastal areas and cities are implementing innovative solutions to mitigate flood risks and enhance resilience. Venice has recently developed a system of storm surge mobile barriers, known as the MoSE (Modulo Sperimentale Elettromeccanico or Experimental Electromechanical Module). This study aims to investigate the economic viability of MoSE operations in light of the potential future evolution of SLR. To conduct a cost-benefit analysis, a system dynamics model is utilised to assess the impact of MoSE operations on economic and residential activities of Venice and its port. Simulations are conducted until the end of the century, considering two SLR scenarios. The results suggest that the economic benefits largely outweigh the combined costs of investment and foregone port revenues resulting from the MoSE closures. Nevertheless, the increasing number of closures due to SLR seriously challenges the viability of the infrastructure in the medium to long term. Even more importantly, very frequent closures will have serious impacts on the quality of the lagoon ecosystem. These findings suggest a revision and stronger integration of the city’s safeguarding strategies, including the increase of the MoSE closure level officially set at 110 cm, and other coordinated interventions, such as sewer system consolidation

Towards a Modelling Process for Simulating Socio-ecosystems with a Focus on Climate Change Adaptation

As the impacts of climate change are expected to be increasingly disruptive, a growing share of the economic literature moved to modelling approaches to address the interconnectedness of social, economic, and environmental issues. Among them, System Dynamics (SD) stands out as a well-established modelling approach to analyse complex social-ecological systems. In order to benefit from such modelling exercises it is necessary to follow a structured process, bearing in mind that models should have as their ultimate ambition that of supporting decision-making processes. Yet, the connection with decision-making is addressed only in the last phases of the modelling process, with emphasis placed only on few particular sectors. Hence, a lack of a general framework that can be used as a reference to address climate change adaptation and which could provide insights to economic valuations to support decision-making processes for a different range of sectors emerges. Consistently, the present study aims to bridge the observed gap by employing a combined SES-DAPSIR framework to build a conceptual modelling process for simulating the behaviour of a generic socio-ecosystem, with a particular focus on climate change adaptation. It also illustrates how the proposed conceptual modelling process is concretely put into practice with an application for a coastal socio-ecosystem. This allows demonstrating how the proposed methodology constitute a potential common starting point for different targeted modelling exercises, resulting particularly useful when moving from analytical modelling to decision support

Towards a Modelling Process for Simulating Socio-ecosystems with a Focus on Climate Change Adaptation

As the impacts of climate change are expected to be increasingly disruptive, a growing share of the economic literature moved to modelling approaches to address the interconnectedness of social, economic, and environmental issues. Among them, System Dynamics (SD) stands out as a well-established modelling approach to analyse complex social-ecological systems. In order to benefit from such modelling exercises it is necessary to follow a structured process, bearing in mind that models should have as their ultimate ambition that of supporting decision-making processes. Yet, the connection with decision-making is addressed only in the last phases of the modelling process, with emphasis placed only on few particular sectors. Hence, a lack of a general framework that can be used as a reference to address climate change adaptation and which could provide insights to economic valuations to support decision-making processes for a different range of sectors emerges. Consistently, the present study aims to bridge the observed gap by employing a combined SES-DAPSIR framework to build a conceptual modelling process for simulating the behaviour of a generic socio-ecosystem, with a particular focus on climate change adaptation. It also illustrates how the proposed conceptual modelling process is concretely put into practice with an application for a coastal socio-ecosystem. This allows demonstrating how the proposed methodology constitute a potential common starting point for different targeted modelling exercises, resulting particularly useful when moving from analytical modelling to decision support

Multi-platform assessment of coastal protection and carbon sequestration in the Venice Lagoon under future scenarios

In recent decades, the rapid development of coastal regions, driven by sustained economic growth and population migration, has amplified their susceptibility to climate-induced hazards. The need to address these challenges in socio-economic coastal hotspots has become a pressing concern, requiring research and analysis to empower local decision-makers to undertake timely and appropriate adaptation measures. Simultaneously, many of these coastal areas boast rich natural habitats, which offer a diverse array of ecosystem services that can enhance climate resilience through both adaptation and mitigation efforts. This study, focuses on the Venice Lagoon, a region particularly vulnerable to natural hazards like sea-level rise, erosion, and flooding due to its low-lying coastal areas, seeks to examine the coastal protection and carbon sequestration services provided by seagrasses and salt marshes. Leveraging the InVEST platform known for its capabilities in valuing ecosystem services and assessing interventions for the protection and restoration of natural capital, this research takes a multi-platform approach by integrating the Coastal Vulnerability and Coastal Blue Carbon models to compute a composite index of these two ecosystem services. Additionally, we incorporate other tools that aid in the computation of the inputs to the InVEST models such as ARIES (Artificial Intelligence for Environment & Sustainability) and the QGIS plugins Molusce and SCP. We also provide estimates of carbon stocks, net carbon sequestration, and the economic value of these habitats for 2040 and 2060. The main outcome of this study is a combined index of coastal protection and carbon sequestration services developed to highlight crucial areas for the provisioning of these services, emphasizing the interconnectedness of socio-ecosystem components in coastal regions. In this study, we highlight the importance of using integrated assessment of ecosystem services in the context of climate change