A new lap in the race to improve our evolutionary fuzzy-controllers for TORCS

Last week we presented at the IEEE Conference on Game 2019, held in London (UK), our new paper titled “Beating uncertainty in racing bot evolution through enhanced exploration and pole position selection“.

The abstract of the work is:

One of the main problems in the design through optimization of car racing bots is the inherent noise in the optimization process: besides the fact that the fitness is a heuristic
based on what we think are the keys to success and as such just a surrogate for the ultimate objective, winning races, fitness itself is uncertain due to the stochastic behavior of racing conditions and the rest of the (simulated) racers. The fuzzy-based genetic controller for the car racing simulator TORCS that we have defined in previous works is based on two fuzzy subcontrollers, one for deciding on the wheel steering angle and another to set the car target speed at the next simulation tick.
They are both optimized by means of an Evolutionary Algorithm, which considers an already tested fitness function focused on the maximization of the average speed during the race and the minimization of the car damage. The noisy environment asks for keeping diversity high during evolution, that is why we have added a Blend Crossover (BLX-alpha) operator, which is, besides, able to exploit current results at the same time it explores. Additionally, we try to address uncertainty in selection by introducing a novel selection policy of parents based in races, where the individuals are grouped and compete against others in several races, so just the firsts ranked will remain in the population as parents. Several experiments have been conducted, testing the value of the different controllers. The results show that the combination of a dynamic BLX-alpha crossover operator plus the pole position selection policy clearly beats the rest of approaches. Moreover, in the comparison of this controller with one of the participants of the prestigious international Simulated Car Racing Championship, our autonomous driver obtains much better results than the opponent.

The presentation can be seen below:

As usual, enjoy it and…cite us! :D

Angry Birds meet EAs at EVO* 2019

Last 24 of April we presented the work “Free Form Evolution for Angry Birds Level Generation” at EVOApplications 2019 (EvoGAMES) a conference part of EVO* 2019, held in Leipzig (Germany).

The abstract of the work is:

This paper presents an original approach for building structures that are stable under gravity for the physics-based puzzle game Angry Birds, with the ultimate objective of creating fun and aesthetically pleasing Angry Birds levels with the minimum number of constraints. This approach consists of a search-based procedural level generation method that uses evolutionary algorithms. In order to evaluate the stability of the levels, they are executed in an adaptation of an open source version of the game called Science Birds. In the same way, an open source evolutionary computation framework has been implemented to fit the requirements of the problem. The main challenge has been to design a fitness function that, first, avoids if possible the actual execution of the simulator, which is time consuming, and, then, to take into account the different ways in which a structure is not structurally sound and consider them in different ways to provide a smooth landscape that eventually achieves that soundness. Different representations and operators have been considered and studied. In order to test the method four experiments have been carried out, obtaining a variety of stable structures, which is the first path for the generation of levels that are aesthetically pleasing as well as playable.

@amorag did a short presentation and later ‘defended’ a poster during the reception act. The presentation is a description of the poster:

Actually the poster was selected as the second best of the conference by the attendants. :D

Those interested can found the paper at Springer web: https://link.springer.com/chapter/10.1007/978-3-030-16692-2_9

Enjoy it… and cite us! ;D

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.

 

 

Improved Genetic Fuzzy Drivers presented at CIG 2018

Last week I presented at IEEE CIG 2018 (held in Maastricht, The Netherlands) our following step in our research about autonomous drivers for Car Racing Simulators, such as TORCS, titled “The Evolutionary Race: Improving the Process of Evaluating Car Controllers in Racing Simulators“.

As commented before by @jjmerelo and later by @fergunet, we designed with Mohammed Salem (University of Mascara) a driver’s AI in which two Fuzzy Subcontrollers were hybridized with a Genetic Algorithm.

In this work we present a better evaluation approach for the GA, combining three methods: heuristic track choosing, improved fitness functions, and race-based selection of the best.

The abstract of the work is:

Simulated car races have been used for a long time as an environment where car controlling algorithms can be tested; they are an interesting testbed for all kinds of algorithms, including metaheuristics such as evolutionary algorithms. However, the challenge in the evolutionary algorithms is to design a reliable and effective evaluation process for the individuals that eventually translates into good solutions to the car racing problem: finding a controller that is able to win in a wide range of tracks and with a good quantity of opponents. Evaluating individual car controllers involves not only the design of a proper fitness function representing how good the car controller would be in a competitive race, but also the selection of the best solution for the optimization problem being solved; this decision might not be easy when uncertainty is present in the problem environment; in this case, weather and track conditions as well as unpredictable behavior of other drivers. Creating a methodology for the automatic design of the controller of an autonomous driver for a car racing simulator such as TORCS is an optimization problem which offers all these challenges. Thus, in this paper we describe an analysis and some proposals to improve the evaluation of optimized fuzzy drivers for TORCS over previous attempts to do so. It builds on preliminary results obtained in previous papers as a baseline and aims to obtain a more competitive autonomous driver via redesign of the fitness evaluation procedure; to this end, two different fitness functions are studied in several experiments, along with a novel race-based approach for the selection of the best individual in the evolution.

And the presentation is:

You can check our paper in the proceedings of the conference.

Enjoy it!

(And cite us as usual :D)

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!!

The_Innkeeper's_Tale_-_The_Innkeeper's_Tale2.jpg

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!!!

Workshop on Spatially Structured Metaheuristics

We cordially invite you to attend the following two-presentations on Spatially Structured Metaheuristics. This mini-workshop will be held at 11.30 a.m. in the CITIC-UGR building (June 26th, 2014).

Spatially Structured Metaheuristics: Principles and Practical Applications
by Juan Luis Jiménez Laredo (University of Luxembourg)

A relevant number of metaheuristics are based on population. Although conventions may establish different names, individuals in evolutionary algorithms, ants in ant colony optimization or particles in particle swarm optimization belong to the same side of a coin: they are all  atomic elements of the population (a.k.a. building-blocks). In this context, spatially structured metaheuristics investigate how to improve the performance of metaheuristics by confining these elements in neighborhoods. This talk aims at presenting the working principles of spatially structured metaheuristics and practical applications to enhance diversity, scalability and robustness.

 

Spatially Structured Metaheuristics: Dynamic and Self-organized Topologies
by Carlos M. Fernandes (University of Lisbon)

Population based metaheuristics are computational search or optimization methods that use a population of possible solutions to a problem. These solutions are able communicate, interact and/or evolve. Two types of strategies for structuring population are possible. In panmictic populations, every individual is allowed to interact with every other individual. In non-panmictic metaheuristics, also called spatially structured population-based metaheuristics, the interaction is restricted to a pre-defined or evolving structure (network). Traditional spatially structured metaheuristics are built on pre-defined static networks of acquaintances over which individuals can interact. However, alternative strategies that overcome some of the difficulties and limitations of static networks (extra design and tuning effort, ad hoc decision policies, rigid connectivity, and lack of feedback from the problem structure and search process) are possible. This talk discusses dynamic topologies for spatially structured metaheuristics and describes a new model for structuring populations into partially connected and self-organized networks. Recent applications of the model on Evolutionary Algorithms and Particle Swarm Optimization are given and discussed.

Evolution using JavaScript in EvoStar

JavaScript, despite its age, is considered now an emergent language, since it is starting to have a ecosystem that allows the development of complex and high-performance applications. That is why in the recent EvoStar we had a poster that uses evolutionary algorithms libraries written using it. It is based on MOOTools to create an object-oriented browser-based library called jsEO, and is entitled An object-oriented library in JavaScript to build modular and flexible cross-platform evolutionary algorithms.