About fergunet

PhD at Computer Science. I like Doctor Who.

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.

Starcraft GP nominated to the HUMIES award

This year we participated in the HUMIES awards with our paper “Towards Automatic StarCraft Strategy Generation Using Genetic Programming“, accepted at CIG2015, wrote in collaboration with Politecnico di Torino and INRA. Our paper was elected from 28 candidates to be part of the 8 finalists, so we can consider it a great achievement. Although we didn’t won, because the astounding quality of the other works, we are thrilled about our nomination :)

Here is the presentation. It even includes a reference to Starship Troopers!

Towards automatic StarCraft strategy generation using genetic programming

I forgot to mention that we published our paper “Towards automatic StarCraft strategy generation using genetic programming” in CIG 2015 conference, held in Taiwan. This was a work made in collaboration with Alberto Tonda (INRA) and Giovanni Squillero (Politecnico di Torino), starting a new research line using this game (and also, starting other nice collaborations that are still a secret!)

The abstract:

Among Real-Time Strategy games few titles have enjoyed the continued success of StarCraft. Many research lines aimed at developing Artificial Intelligences, or “bots”, capable of challenging human players, use StarCraft as a platform. Several characteristics make this game particularly appealing for researchers, such as: asymmetric balanced factions, considerable complexity of the technology trees, large number of units with unique features, and potential for optimization both at the strategical and tactical level. In literature, various works exploit evolutionary computation to optimize particular aspects of the game, from squad formation to map exploration; but so far, no evolutionary approach has been applied to the development of a complete strategy from scratch. In this paper, we present the preliminary results of StarCraftGP, a framework able to evolve a complete strategy for StarCraft, from the building plan, to the composition of squads, up to the set of rules that define the bot’s behavior during the game. The proposed approach generates strategies as C++ classes, that are then compiled and executed inside the OpprimoBot open-source framework. In a first set of runs, we demonstrate that StarCraftGP ultimately generates a competitive strategy for a Zerg bot, able to defeat several human-designed bots.

Do you want to know more? Download the paper draft or electronic version in IEEE web.

This was a triumph: Evolving intelligent bots for videogames. And for Science.

Today I gave the talk entitled “This was a triumph: Evolving intelligent bots for videogames. And for Science” to the students of the High School IES Montes Orientales. I talked about the concept of Evolutionary Algorithms, and then I presented some of our results of their application in games such as Planet Wars, Unreal, Starcraft or Content Generation.

Other members of the ETSIIT gave other amazing talks about Free Software, Artificial Vision, Robotics or Language Processing. Hopefully we will get some of these student working with us in several years!

 

[Paper] Studying the effect of population size in distributed evolutionary algorithms on heterogeneous clusters

Finally we have published in Applied Soft Computer journal the last paper related with my thesis. We used OSGiLiath to perform a number of cool experiments related with automatic adaptation of dEAs on heterogeneous clusters.

The abstract:

Distributed Evolutionary Algorithms are traditionally executed on homogeneous dedicated clusters, despite most scientists have access mainly to networks of heterogeneous nodes (for example, desktop PCs in a lab). Fitting this kind of algorithms to these environments, so that they can take advantage of their heterogeneity to save running time, is still an open problem. The different computational power of the nodes affects the performance of the algorithm, and tuning or fitting it to each node properly could reduce execution time.

Since the distributed Evolutionary Algorithms include a whole range of parameters that influence the performance, this paper proposes a study on the population size. This parameter is one of the most important, since it has a direct relationship with the number of iterations needed to find the solution, as it affects the exploration factor of the algorithm. The aim of this paper consists in validating the following hypothesis: fitting the sub-population size to the computational power of the heterogeneous cluster node can lead to an improvement in running time with respect to the use of the same population size in every node.

Two parameter size schemes have been tested, an offline and an online parameter setting, and three problems with different characteristics and computational demands have been used.

Results show that setting the population size according to the computational power of each node in the heterogeneous cluster improves the time required to obtain the optimal solution. Meanwhile, the same set of different size values could not improve the running time to reach the optimum in a homogeneous cluster with respect to the same size in all nodes, indicating that the improvement is due to the interaction of the different hardware resources with the algorithm. In addition, a study on the influence of the different population sizes on each stage of the algorithm is presented. This opens a new research line on the fitting (offline or online) of parameters of the distributed Evolutionary Algorithms to the computational power of the devices.

The paper (currently “In press” form) can be downloaded from http://www.sciencedirect.com/science/article/pii/S1568494615006468

GeNeura en la Noche de los Investigadores 2015

GeNeura estuvo presente dentro de la Noche Europea de los Investigadores en el stand de la actividad El Ojo que todo lo ve. En este evento estuvimos realizando difusión de los proyectos SIPESCA, MOSOS, PETRA y el recientemente solicitado SiMoPE. Dentro del stand estuvimos mostrando el estado de los proyectos, sus avances, los dispositivos de captación, y los datos de dispositivos móviles que habíamos ido tomando. Tuvo una gran afluencia de público, que participó de forma activa a través de preguntas y opiniones. Además, estuvimos midiendo el paso de las personas que estuvieron asistiendo al evento con uno de nuestros dispositivos.

Algunas fotos del evento:

La Noche Europea de los Investigadores 2015Noche Europea de l@s Investigador@s 2015

La Noche Europea de los Investigadores 2015

Muchas gracias a todos los asistentes!

Evostar 2015 mandatory post

We can never skip the chance to assist the Evostar conference, and aside learn the latest trends in Evolutionary Computation and present our results, we also have a good time with our other colleagues.

This time the conference was held in Copenhagen (Denmark), and because Antonio and me were part of the organization we didn’t have much time to go sightseeing, but we went to Tivoli Gardens and ride the flying chairs (and screamed like babies).

On the scientific part, we presented two papers to EvoGames track, related with our research lines on content generation for videogames and AI optimization. The first paper, How the World Was MADE: Parametrization of Evolved Agent-Based Models for Backstory Generation, presents a study on parametrization of the values that define a virtual world to facilitate the emergence of archetypes, and be able to generate interesting backstories (for videogames, for example). See the poster here:

The poster

Also, as we are commited to open science and open software, you can download the MADE environment from its web. The abstract:

Generating fiction environments for a multi-agent system optimized by genetic algorithms (with some specific requirements related to the desirable plots), presents two main problems: first it is impossible to know in advance the optimal value for the particular designed fitness function, and at the same time, it creates a vast search space for the parameters that it needs. The purpose of this paper is to define a methodology to find the best parameter values for both, the evolutionary algorithm, and the own fictional world configuration. This design includes running, to completion, a world simulation represented as a chromosome, and assigning a fitness to it, thus composing a very complex fitness landscape.
In order to optimize the resources allocated to evolution and to have some guarantees that the final result will be close to the optimum, we systematically analyze a set of possible values of the most relevant parameters, obtaining a set of generic rules. These rules, when applied to the plot requisites, and thus, to the fitness function, will lead to a reduced range of parameter values that will help the storyteller to create optimal worlds with a reduced computation budget.

Evostar 2015 - Copenhagen(That’s me with the IKEA rat plushies I used to describe our system)

Our other paper, It’s Time to Stop: A Comparison of Termination Conditions in the Evolution of Game Bots, describes a methodology to compare different termination conditions in noisy environments such as the RTS games. The abstract:

Evolutionary Algorithms (EAs) are frequently used as a mechanism for the optimization of autonomous agents in games (bots), but knowing when to stop the evolution, when the bots are good enough, is not as easy as it would a priori seem. The first issue is that optimal bots are either unknown (and thus unusable as termination condition) or unreachable. In most EAs trying to find optimal bots fitness is evaluated through game playing. Many times it is found to be noisy, making its use as a termination condition also complicated. A fixed amount of evaluations or, in the case of games, a certain level of victories does not guarantee an optimal result. Thus the main objective of this paper is to test several termination conditions in order to find the one that yields optimal solutions within a restricted amount of time, and that allows researchers to compare different EAs as fairly as possible. To achieve this we will examine several ways of finishing an EA who is finding an optimal bot design process for a particular game, Planet Wars in this case, with the characteristics described above, determining the capabilities of every one of them and, eventually, selecting one for future designs.

Evostar 2015 - Copenhagen(Here’s Antonio presenting the paper)

You can see the rest of the Evostar photos in their flickr account.