# Algoritmos evolutivos aplicados a HearthStone en las noticias

Este fin de semana algunas webs se han hecho eco de una noticia originada por la Agencia SINC.

En concreto la noticia se ha titulado «La inteligencia artificial imita la evolución biológica para ganar en los videojuegos« y resumida como:

Informáticos españoles han aplicado técnicas avanzadas de inteligencia artificial para generar automáticamente los mejores mazos de cartas en Hearthstone, un videojuego en línea con más de 30 millones de jugadores en todo el mundo. Los algoritmos se inspiran en el proceso biológico de la selección natural.

Entre las webs que la han recogido, destacan, por ejemplo la de La Sexta.

Dichas noticias se refieren a nuestro trabajo publicado hace algunos meses en la revista Knowledge-Based Systems y comentado en esta entrada del blog.

# Creating Hearthstone decks by using Genetic Algorithms

I’m glad you’re here, friend! There’s a chill outside, so pull up a chair by the hearth of our inn and prepare to learn how the Ancient Gods use the power of the secret and ancient branch of the Evolution to generate Hearthstone decks by means of the magic and mistery!!

Several months ago, my colleague Alberto Tonda and I were discussing about our latest adventures playing the Digital Collectible Card Game Hearthstone, when one of us said «Uhm, Genetic Algorithms usually work well with combinatorial problems, and solutions are usually a vector of elements. Elements such as cards. Such as cards of Hearthstone, the game we are playing right now while we are talking. Are you thinking what I’m thinking?»

Five minutes later we found an open-source Hearthstone simulator and started to think how to address the possibility of automatically evolve decks of Hearthstone.

The idea is quite simple: Hearthstone is played using a deck of 30 cards (from a pool of thousands available), so it is easy to model the candidate solution. With the simulator, we can perform several matches using different enemy decks, and obtain the number of victories. Therefore, we have a number that can be used to model the performance (fitness) of the deck.

Soooo, it’s easy to see one and one makes two, two and one makes three, and it was destiny, that we created a genetic algorithm that generates deck for Hearthstone for free.

Our preliminary results where discussed here, but we wanted to continue testing our method, so we tested using all available classes of the game, with the help of JJ, Giovanny and Antonio. All the best human-made decks were outperformed by our approach! And not only that, we applied a new operator called Smart Mutation that it is based in what we do when we test new decks in Hearthstone: we remove a card, and place another instead, but with +/-1 mana crystals, and not one completely random from the pool. The results were even better. Neat!

Maybe you prefer to read the abstract, that it is written in a more formal way than this post. You know, using the language of the science.

Collectible card games have been among the most popular and profitable products of the entertainment industry since the early days of Magic: The Gathering in the nineties. Digital versions have also appeared, with HearthStone: Heroes of WarCraft 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.

You can download the complete paper from the Knowledge-based Systems Journal https://www.sciencedirect.com/science/article/pii/S0950705118301953

See you in future adventures!!!

# Master of Evolution! Using Genetic Algorithms to generate decks for the game HearthStone

This September we attended to the IEEE CIG 2017 Conference in Santorini, Greece, to present the paper «Evolutionary Deckbuilding in HearthStone». This paper was written in collaboration with our colleagues Alberto Tonda and Giovanni Squillero.

The story of this paper started a (not so) long time ago while me and Alberto were discussing about how awesome HearthStone is. Suddenly, we thought about how easy it would be to create the constraints for the uGP framework, and that there were some open source simulators of the game. In a while, we already had the constraints, the simulator adapted to accept individuals from uGP, and some experiments running.

And we finished the paper after, of course.

You can download the paper draft from here (the electronic original version is not available yet).

And here is the presentation:

The abstract:

—One of the most notable features of collectible card games is deckbuilding, that is, defining a personalized deck before the real game. Deckbuilding is a challenge that involves a big and rugged search space, with different and unpredictable behaviour
after simple card changes and even hidden information. In this paper, we explore the possibility of automated deckbuilding: a genetic algorithm is applied to the task, with the evaluation delegated to a game simulator that tests every potential deck against a varied and representative range of human-made decks.
In these preliminary experiments, the approach has proven able to create quite effective decks, a promising result that proves that, even in this challenging environment, evolutionary algorithms can find good solutions.