Automated Playtesting in Collectible Card Games using Evolutionary Algorithms: a Case Study in HearthStone

Collectible card games have been among the most popular and profitable products of the entertainment industry since the early days of Magic: The GatheringTM in the nineties. Digital versions have also appeared, with HearthStone: Heroes of WarCraftTM being one of the most popular. In Hearthstone, every player can play as a hero, from a set of nine, and build his/her deck before the game from a big pool of available cards, including both neutral and hero-specific cards. This kind of games offers several challenges for researchers in artificial intelligence since they involve hidden information, unpredictable behaviour, and a large and rugged search space. Besides, an important part of player engagement in such games is a periodical input of new cards in the system, which mainly opens the door to new strategies for the players. Playtesting is the method used to check the new card sets for possible design flaws, and it is usually performed manually or via exhaustive search; in the case of Hearthstone, such test plays must take into account the chosen hero, with its specific kind of cards. In this paper, we present a novel idea to improve and accelerate the playtesting process, systematically exploring the space of possible decks using an Evolutionary Algorithm (EA). This EA creates HearthStone decks which are then played by an AI versus established human-designed decks. Since the space of possible combinations that are play-tested is huge, search through the space of possible decks has been shortened via a new heuristic mutation operator, which is based on the behaviour of human players modifying their decks. Results show the viability of our method for exploring the space of possible decks and automating the play-testing phase of game design. The resulting decks, that have been examined for balancedness by an expert player, outperform human-made ones when played by the AI; the introduction of the new heuristic operator helps to improve the obtained solutions, and basing the study on the whole set of heroes shows its validity through the whole range of decks

Monte Carlo -puuhakua käyttävien tekoälymenetelmien soveltuvuus vuoropohjaisiin strategiapeleihin

Tässä tutkielmassa tarkastellaan Monte Carlo -puuhaun soveltuvuutta vuoropohjaisten strategiapelien tekoälyratkaisuihin kirjallisuuskatsausta hyödyntäen. Aluksi esitellään sekä minimax-algoritmi että Monte Carlo -puuhaku suosittuine muunnelmineen, ja sen jälkeen perehdytään tarkemmin neljään vuoropohjaiseen strategiapeliin: Shakkiin, go-lautapeliin, pokeriin ja Magic: The Gathering -keräilykorttipeliin. Kunkin neljän pelin kohdalla tutustutaan kyseisen pelin tekoälylle asettamiin haasteisiin, olemassa oleviin tekoälyratkaisuihin ja etenkin Monte Carlo -puuhakua hyödyntäviin tekoälytoimijoihin. Lopuksi luodaan vielä lyhyt katsaus joukkoon Monte Carlo -puuhakua hyödyntäviä tekoälyratkaisuja muiden vuoropohjaisten strategiapelien kohdalla. Huomataan, että Monte Carlo -puuhakua käyttämällä saavutetaan sen yleispätevän luonteen vuoksi usein merkittäviä hyötyjä etenkin sellaisissa peleissä, joille mielekkään evaluaatiofunktion kirjoittaminen on hankalaa

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields

Sparsity-aware neural user behavior modeling in online interaction platforms

Modern online platforms offer users an opportunity to participate in a variety of content-creation, social networking, and shopping activities. With the rapid proliferation of such online services, learning data-driven user behavior models is indispensable to enable personalized user experiences. Recently, representation learning has emerged as an effective strategy for user modeling, powered by neural networks trained over large volumes of interaction data. Despite their enormous potential, we encounter the unique challenge of data sparsity for a vast majority of entities, e.g., sparsity in ground-truth labels for entities and in entity-level interactions (cold-start users, items in the long-tail, and ephemeral groups). In this dissertation, we develop generalizable neural representation learning frameworks for user behavior modeling designed to address different sparsity challenges across applications. Our problem settings span transductive and inductive learning scenarios, where transductive learning models entities seen during training and inductive learning targets entities that are only observed during inference. We leverage different facets of information reflecting user behavior (e.g., interconnectivity in social networks, temporal and attributed interaction information) to enable personalized inference at scale. Our proposed models are complementary to concurrent advances in neural architectural choices and are adaptive to the rapid addition of new applications in online platforms. First, we examine two transductive learning settings: inference and recommendation in graph-structured and bipartite user-item interactions. In chapter 3, we formulate user profiling in social platforms as semi-supervised learning over graphs given sparse ground-truth labels for node attributes. We present a graph neural network framework that exploits higher-order connectivity structures (network motifs) to learn attributed structural roles of nodes that identify structurally similar nodes with co-varying local attributes. In chapter 4, we design neural collaborative filtering models for few-shot recommendations over user-item interactions. To address item interaction sparsity due to heavy-tailed distributions, our proposed meta-learning framework learns-to-recommend few-shot items by knowledge transfer from arbitrary base recommenders. We show that our framework consistently outperforms state-of-art approaches on overall recommendation (by 5% Recall) while achieving significant gains (of 60-80% Recall) for tail items with fewer than 20 interactions. Next, we explored three inductive learning settings: modeling spread of user-generated content in social networks; item recommendations for ephemeral groups; and friend ranking in large-scale social platforms. In chapter 5, we focus on diffusion prediction in social networks where a vast population of users rarely post content. We introduce a deep generative modeling framework that models users as probability distributions in the latent space with variational priors parameterized by graph neural networks. Our approach enables massive performance gains (over 150% recall) for users with sparse activities while being faster than state-of-the-art neural models by an order of magnitude. In chapter 6, we examine item recommendations for ephemeral groups with limited or no historical interactions together. To overcome group interaction sparsity, we present self-supervised learning strategies that exploit the preference co-variance in observed group memberships for group recommender training. Our framework achieves significant performance gains (over 30% NDCG) over prior state-of-the-art group recommendation models. In chapter 7, we introduce multi-modal inference with graph neural networks that captures knowledge from multiple feature modalities and user interactions for multi-faceted friend ranking. Our approach achieves notable higher performance gains for critical populations of less-active and low degree users

Assessment of Renewable Energy Resources with Remote Sensing

The development of renewable energy sources plays a fundamental role in the transition towards a low carbon economy. Considering that renewable energy resources have an intrinsic relationship with meteorological conditions and climate patterns, methodologies based on the remote sensing of the atmosphere are fundamental sources of information to support the energy sector in planning and operation procedures. This Special Issue is intended to provide a highly recognized international forum to present recent advances in remote sensing to data acquisition required by the energy sector. After a review, a total of eleven papers were accepted for publication. The contributions focus on solar, wind, and geothermal energy resource. This editorial presents a brief overview of each contribution.About the Editor .............................................. vii Fernando Ramos Martins Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing Reprinted from: Remote Sens. 2020, 12, 3748, doi:10.3390/rs12223748 ................. 1 André R. Gonçalves, Arcilan T. Assireu, Fernando R. Martins, Madeleine S. G. Casagrande, Enrique V. Mattos, Rodrigo S. Costa, Robson B. Passos, Silvia V. Pereira, Marcelo P. Pes, Francisco J. L. Lima and Enio B. Pereira Enhancement of Cloudless Skies Frequency over a Large Tropical Reservoir in Brazil Reprinted from: Remote Sens. 2020, 12, 2793, doi:10.3390/rs12172793 ................. 7 Anders V. Lindfors, Axel Hertsberg, Aku Riihelä, Thomas Carlund, Jörg Trentmann and Richard Müller On the Land-Sea Contrast in the Surface Solar Radiation (SSR) in the Baltic Region Reprinted from: Remote Sens. 2020, 12, 3509, doi:10.3390/rs12213509 ................. 33 Joaquín Alonso-Montesinos Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera Reprinted from: Remote Sens. 2020, 12, 1382, doi:10.3390/rs12091382 ................. 43 Román Mondragón, Joaquín Alonso-Montesinos, David Riveros-Rosas, Mauro Valdés, Héctor Estévez, Adriana E. González-Cabrera and Wolfgang Stremme Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area Reprinted from: Remote Sens. 2020, 12, 1212, doi:10.3390/rs12071212 ................. 61 Jinwoong Park, Jihoon Moon, Seungmin Jung and Eenjun Hwang Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island Reprinted from: Remote Sens. 2020, 12, 2271, doi:10.3390/rs12142271 ................. 79 Guojiang Xiong, Jing Zhang, Dongyuan Shi, Lin Zhu, Xufeng Yuan and Gang Yao Modified Search Strategies Assisted Crossover Whale Optimization Algorithm with Selection Operator for Parameter Extraction of Solar Photovoltaic Models Reprinted from: Remote Sens. 2019, 11, 2795, doi:10.3390/rs11232795 ................. 101 Alexandra I. Khalyasmaa, Stanislav A. Eroshenko, Valeriy A. Tashchilin, Hariprakash Ramachandran, Teja Piepur Chakravarthi and Denis N. Butusov Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning Reprinted from: Remote Sens. 2020, 12, 3420, doi:10.3390/rs12203420 ................. 125 Ian R. Young, Ebru Kirezci and Agustinus Ribal The Global Wind Resource Observed by Scatterometer Reprinted from: Remote Sens. 2020, 12, 2920, doi:10.3390/rs12182920 ................. 147 Susumu Shimada, Jay Prakash Goit, Teruo Ohsawa, Tetsuya Kogaki and Satoshi Nakamura Coastal Wind Measurements Using a Single Scanning LiDAR Reprinted from: Remote Sens. 2020, 12, 1347, doi:10.3390/rs12081347 ................. 165 Cristina Sáez Blázquez, Pedro Carrasco García, Ignacio Martín Nieto, MiguelAngel ´ Maté-González, Arturo Farfán Martín and Diego González-Aguilera Characterizing Geological Heterogeneities for Geothermal Purposes through Combined Geophysical Prospecting Methods Reprinted from: Remote Sens. 2020, 12, 1948, doi:10.3390/rs12121948 ................. 189 Miktha Farid Alkadri, Francesco De Luca, Michela Turrin and Sevil Sariyildiz A Computational Workflow for Generating A Voxel-Based Design Approach Based on Subtractive Shading Envelopes and Attribute Information of Point Cloud Data Reprinted from: Remote Sens. 2020, 12, 2561, doi:10.3390/rs12162561 ................. 207Instituto do Ma

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov