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Currently available papers

Learning the Rules of the Game: An Interpretable AI for Learning How to Play

In this article, we present an interpretable artificial intelligence, and its associated machine learning algorithm, that is capable of automatically learning the rules of a game whenever the rules—the relationship between a player’s current state …

Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning

Reinforcement learning (RL) combined with deep neural networks has performed remarkably well in many genres of games recently. It has surpassed human-level performance in fixed game environments and turn-based two-player board games. However, to the …

DAE-GP: denoising autoencoder LSTM networks as probabilistic models in estimation of distribution genetic programming

Estimation of distribution genetic programming (EDA-GP) algorithms are metaheuristics where sampling new solutions from a learned probabilistic model replaces the standard mutation and recombination operators of genetic programming (GP). This paper …

Synthesis through unification genetic programming

We present a new method, Synthesis through Unification Genetic Programming (STUN GP), which synthesizes provably correct programs using a Divide and Conquer approach. This method first splits the input space by undergoing a discovery phase that uses …

Optimizing Hearthstone agents using an evolutionary algorithm

Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. This game genre allows researchers to deal with hidden information, uncertainty …

Spatial Evolutionary Generative Adversarial Networks

Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse. These pathologies mainly arise from a lack of diversity in their adversarial interactions. Evolutionary generative adversarial networks apply …

On botnet detection with genetic programming under streaming data label budgets and class imbalance

Algorithms for constructing models of classification under streaming data scenarios are becoming increasingly important. In order for such algorithms to be applicable under ‘real-world’ contexts we adopt the following objectives: 1) operate under …

Monte Carlo tree search experiments in hearthstone

In this paper, we introduce a Monte-Carlo tree search (MCTS) approach for the game "Hearthstone: Heroes of Warcraft". We argue that, in light of the challenges posed by the game (such as uncertainty and hidden information), Monte Carlo tree search …

HearthBot\: An Autonomous Agent Based on Fuzzy ART Adaptive Neural Networks for the Digital Collectible Card Game HearthStone

Digital collectible card games, as partially observable games based on alternating turns, such as HearthStone, have been the most played card games in recent years, where the main challenge is the creation of strategies capable of subdue the enemy's …

Evolutionary Deckbuilding in HearthStone

One of the most notable features of collectible cardgames is deckbuilding, that is, defining a personalized deck beforethe real game. Deckbuilding is a challenge that involves a big andrugged search space, with different and unpredictable …