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 …
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 …
Generative adversarial networks (GANs) achieved relevant advances in the field of generative algorithms, presenting high-quality results mainly in the context of images. However, GANs are hard to train, and several aspects of the model should be …
Society has come to rely on algorithms like classifiers for important decision making, giving rise to the need for ethical guarantees such as fairness. Fairness is typically defined by asking that some statistic of a classifier be approximately equal …
Genetic Improvement (GI) focuses on the development of evolutionary methods to automate software engineering tasks, such as performance improvement or software bugs removal. This paper explores the use of Novelty Search (NS) with GenProg, since it …
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 …
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 …
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 …
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 …
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 …