This paper presents a novel car racing controller for TORCS (The Open Racing Car Simulator), which is based in the combination of two fuzzy subcontrollers, one for setting the speed, and one to control the steer angle. The obtained results are quite promissing, as the controller is quite competitive even against very tough TORCS teams.
The abstract of the paper is:
“When driving a car it is essential to take into account all possible factors; even more so when, like in the TORCS simulated race game, the objective is not only to avoid collisions, but also to win the race within a limited budget. In this paper, we present the design of an autonomous driver for racing car in a simulated race. Unlike previous controllers, that only used fuzzy logic approaches for either acceleration or steering, the proposed driver uses simultaneously two fuzzy controllers for steering and computing the target speed of the car at every moment of the race. They use the track border sensors as inputs and besides, for enhanced safety, it has also taken into account the relative position of the other competitors. The proposed fuzzy driver is evaluated in practise and timed races giving good results across a wide variety of racing tracks, mainly those that have many turning points.”
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.
—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.
Our latest publication My life as a sim: evolving unique and engaging life stories using virtual worlds, using our framework MADE (created by @rubenhek), has been published in the ALIFE 2014 conference. The abstract:
Stories are not only painfully weaved by crafty writers in the solitude of their studios; they also have to be produced massively for non-player characters in the video game industry or tailored to particular tastes in personalized stories. However, the creation of fictional stories is a very complex task that usually implies a creative process where the author has to combine characters, conflicts and backstories to create an engaging narrative. This work describes a general methodology to generate cohesive and coherent backstories where desired archetypes (universally accepted literary symbols) can emerge in complex stochastic systems. This methodology supports the modeling and parametrization of the agents, the environment where they will live and the desired literary setting. The use of a Genetic Algorithm (GA) is proposed to establish the parameter configuration that will lead to backstories that best fit the setting. Information extracted from a simulation can then be used to create the literary work. To demonstrate the adequacy of the methodology, we perform an implementation using a specific multi-agent system and evaluate the results, testing with three different literary settings.
As important as finding a solution to the game of MasterMind that is better than anyone else is to find one that can be applied to a wide range of sizes. In this paper we get rid of a parameter, the limit size of the consistent set we use for scoring every combination. This makes a faster algorithm, and not always worse than the optimal consistent set size.
Since nobody is reviewing this, I’m not giving a long answer this question posed by one of the reviewers of a paper I submitted ages ago, in the nineties, proposing solutionsusing evolutionary algorithms to the game. However, the short question, as happens with most everything in Science, it’s because it’s there. However, after this empirical study of exhaustive solutions it is a bit closer.
In this paper, which is a draft of something we intend to submit to a journal in the near future, we describe something that has been disregarded in Mastermind solutions: the pure chance of drawing the correct solution in the opening moves. In fact, this chance dominates in the first moves, until the search space is reduced to a single solution, which is usually the intention of most empirical solutions to the game.
Which means that a method that is able to increase that chance, will be able to beat traditional solutions. In fact, it does not, but it is consistently as good as the best solution for each size.
It is rather a longish paper (and might become even more so before submission), but you might learn a thing or two about Mastermind. Besides, it is intended as a base for future papers that will apply our usual techniques, evolutionary algorithms.