Gene regulated car driving: using a gene regulatory network to drive a virtual car

This paper presents a virtual racing car controller based on an artificial gene regulatory network. Usually used to control virtual cells in developmental models, recent works showed that gene regulatory networks are also capable to control various kinds of agents such as foraging agents, pole cart, swarm robots, etc. This paper details how a gene regulatory network is evolved to drive on any track through a three-stages incremental evolution. To do so, the inputs and outputs of the network are directly mapped to the car sensors and actuators. To make this controller a competitive racer, we have distorted its inputs online to make it drive faster and to avoid opponents. Another interesting property emerges from this approach: the regulatory network is naturally resistant to noise. To evaluate this approach, we participated in the 2013 simulated racing car competition against eight other evolutionary and scripted approaches. After its first participation, this approach finished in third place in the competition

Gene regulated car driving: using a gene regulatory network to drive a virtual car

This paper presents a virtual racing car controller based on an artificial gene regulatory network. Usually used to control virtual cells in developmental models, recent works showed that gene regulatory networks are also capable to control various kinds of agents such as foraging agents, pole cart, swarm robots, etc. This paper details how a gene regulatory network is evolved to drive on any track through a three-stages incremental evolution. To do so, the inputs and outputs of the network are directly mapped to the car sensors and actuators. To make this controller a competitive racer, we have distorted its inputs online to make it drive faster and to avoid opponents. Another interesting property emerges from this approach: the regulatory network is naturally resistant to noise. To evaluate this approach, we participated in the 2013 simulated racing car competition against eight other evolutionary and scripted approaches. After its first participation, this approach finished in third place in the competition

Computación evolutiva aplicada al desarrollo de videojuegos: Mario AI

A lo largo de la historia el juego y los videojuegos han tenido una evolución paralela, actualmente han evolucionado considerablemente hasta convertirse en productos de alta tecnología. En el caso de los videojuegos se han convertido en una industria muy fuerte con un volumen de negocio comparable al cinematográfico. Hasta hace unos años, el desarrollo de los videojuegos se ha centrado en el apartado gráfico y el apartado sonoro, dejando a un segundo plano el comportamiento de los NPCs (Non Player Character). En la actualidad está habiendo una tendencia a centrarse cada vez más en la inteligencia artificial (IA) de los NPCs, que da lugar a numerosos avances e investigaciones relacionadas con la IA con el objetivo de proporcionar a los usuarios de videojuegos un comportamiento variable e impredecible de los NPC tanto en los enemigos como compañeros que se encuentra el usuario a lo largo del videojuego. El presente proyecto se centra en aplicar alguna de las técnicas de IA existentes a un videojuego, para ello se ha realizado un estudio con diferentes videojuegos en los cuales se pueden aplicar técnicas de IA, seguidamente se hará una elección justificada de uno de los videojuegos analizados. A continuación se determinarán que técnicas son susceptibles a aplicar al videojuego elegido, y se elegirá una de estas técnicas, en esta investigación se ha elegido la técnica de IA Algoritmos Genéticos (AG) dentro de la Computación Evolutiva, y Mario AI como el videojuego a probar. La novedad de esta investigación reside en que se desarrolla un agente autónomo e inteligente capaz de completar varios niveles del videojuego en cuestión, mediante la utilización de los AGs. El resultado obtenido tras la realización del proyecto ha sido exitoso. Se ha comprobado que los AGs son apropiados en la creación de agentes que son capaces de superar diferentes niveles de Mario AI con dificultad variable. Para comprobar finalmente la calidad de la solución se decide participar en la competición Mario AI Championship 2011, que se celebrará el próximos mes de noviembre, en GIC2011. Al compararse con los resultados de años anteriores se ha verificado que el agente desarrollado en el presente proyecto obtiene puntuaciones mayores que los participantes de la competición en años anteriores. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Throughout history the games and videogames have had parallel evolution, nowadays it have evolved considerably until it becomes high-tech products. Videogames has become a strong industry with a turnover comparable with movie industry. Until recently, the videogames development has focused on the graphics and sound sections, leaving the background the behavior of NPCs (Non Player Character). But now, there is a tendency to focus increasingly on the Artificial Intelligence (AI) of NPCs, so it makes many advances and researches related to AI in order to provide videogames users variables and unpredictable behaviors of enemies and partners NPCs throughout the videogame. This project focuses on applying some of the existing IA techniques to a videogame, it has made a study with different videogames which can apply these AI techniques, and afterward it has made a justified election of one of the analyzed videogames. Next, it has determined that IA techniques can be to apply to chosen videogame, and it has chosen one of these techniques, this research has chosen the Genetic Algorithms (GA) in Computation Evolutionary as the AI technique, and Mario AI as the videogame to try. The innovation of this research is to develop an intelligent and autonomous agent that is capable of completing various levels of the videogame in question through the use of GAs. The obtained results after of making the project have been successful. There is evidence that GAs are appropriate in the creation of agents that able to overcome different levels with varying difficulty of Mario AI. To check finally the quality of the solution it has decided to take part in the "Mario AI Championship 2010" competition, to be held the next November in GIC2011. When it has compared with the previous years results, it has verified that the developed agent, in this project, gets higher scores that the participants of the competition in previous years.Ingeniería en Informátic

Enhancing player experience in computer games: A computational Intelligence approach.

Ph.DDOCTOR OF PHILOSOPH

Event and state detection in time series by genetic programming

Event and state detection in time series has significant value in scientific areas and real-world applications. The aim of detecting time series event and state patterns is to identify particular variations of user-interest in one or more channels of time series streams. For example, dangerous driving behaviours such as sudden braking and harsh acceleration can be detected from continuous recordings from inertial sensors. However, the existing methods are highly dependent on domain knowledge such as the size of the time series pattern and a set of effective features. Furthermore, they are not directly suitable for multi-channel time series data. In this study, we establish a genetic programming based method which can perform classification on multi-channel time series data. It does not need the domain knowledge required by the existing methods. The investigation consists of four parts: the methodology, an evaluation on event detection tasks, an evaluation on state detection tasks and an analysis on the suitability for real-world applications. In the methodology, a GP based method is proposed for processing and analysing multi-channel time series streams. The function set includes basic mathematical operations. In addition, specific functions and terminals are introduced to reserve historical information, capture temporal dependency across time points and handle dependency between channels. These functions and terminals help the GP based method to automatically find the pattern size and extract features. This study also investigates two different fitness functions - accuracy and area under the curve. The proposed method is investigated on a range of event detection tasks. The investigation starts from synthetic tasks such as detecting complete sine waves. The performance of the GP based method is compared to traditional classification methods. On the raw data the GP based method achieves 100 percent accuracy, which outperforms all the non-GP methods.The performance of the non-GP methods is comparable to the GP based method only with suitable features. In addition, the GP based method is investigated on two complex real-world event detection tasks - dangerous driving behaviour detection and video shot detection. In the task of detecting three dangerous driving behaviours from 21-channel time series data, the GP based method performs consistently better than the non-GP classifiers even when features are provided. In the video shot detection task, the GP based method achieves comparable performance on 11200-channel time series to the non-GP classifiers on 28 features. The GP based method is more accurate than a commercial product. The GP based method has also been investigated on state detection tasks. This involves synthetic tasks such as detecting concurrent high values in four of five channels and a real-world activity recognition problem. The results also show that the GP based method consistently outperforms the non-GP methods even with the presence of manually constructed features. As part of the investigation, a mobile phone based activity recognition data set was collected as there was no existing publicly available data set. The suitability of the GP based method for solving real-world problems is further analysed. Our analysis shows that the GP based method can be successfully extended for multi-class classification. The analysis of the evolved programs demonstrates that they do capture time series patterns. On synthetic data sets, the injected regularities are revealed in understandable individuals. The best programs for three real-world problems are more difficult to explain but still provide some insight. The selection of relevant channels and data points by the programs are consistent with domain knowledge. In addition, the analysis shows that the proposed method still performs well for time series pattern of different sizes. The effective window sizes of the evolved GP programs are close to the pattern size. Finally, our study on execution performance of the evolved programs shows that these programs are fast in execution and are suitable for real-time applications. In summary, the GP based method is suitable for the kinds of real-world applications studied in this thesis. This thesis concludes that, with a suitable representation, genetic programming can be an effective method for event and state detection in multi-channel time series for a range of synthetic and real-world tasks. This method does not require much domain knowledge such as the pattern size and suitable features. It offers an effective classification method in similar tasks that are studied in this thesis