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Papers by Raul Lara-Cabrera

2014 IEEE Congress on Evolutionary Computation (CEC), 2014

Lecture Notes in Computer Science, 2014

Procedural content generation (PCG) is the programmatic generation of game content using a random... more Procedural content generation (PCG) is the programmatic generation of game content using a random or pseudo-random process that results in an unpredictable range of possible game play spaces. This methodology brings many advantages to game developers, such as reduced memory consumption. In this paper we introduce a procedural map generator for a real-time strategy (RTS) game. The main component of this generator is a genetic algorithm devoted to create and evolve balanced maps, i.e. maps where no player has any map related advantage with respect to other players. The selected RTS game is called Planet Wars and it was used in the Google AI Challenge 2010. It is a space conquest game whose objective is to take over all the planets on the map.

Entertainment Computing, 2014

ABSTRACT This paper presents a procedural content generation (PCG) method that is able to generat... more ABSTRACT This paper presents a procedural content generation (PCG) method that is able to generate aesthetic maps for a real-time strategy game. The maps were characterized based on either their geometrical properties or their topological measures (obtained in this latter case from the sphere-of-influence graph induced by each map). Using these features, a distance function between maps can be defined. This function is used in turn to determine how close/far each map generated by the PCG method (a self-adaptive evolutionary algorithm) is to a collection of maps which were taken initially to be aesthetic or non-aesthetic. This correspondence guided a multi-objective evolutionary approach whereby maps close to aesthetic maps and far to non-aesthetic maps are sought. Self-organizing maps are used to ascertain whether the so-generated maps naturally cluster together with aesthetic maps, as well as to provide a qualitative assessment of the ability of each set of features to characterize the latter.

International Journal of Interactive Multimedia and Artificial Intelligence, 2015

Videogames are one of the most important and profitable sectors in the industry of entertainment.... more Videogames are one of the most important and profitable sectors in the industry of entertainment. Nowadays, the creation of a videogame is often a large-scale endeavor and bears many similarities with, e.g., movie production. On the central tasks in the development of a videogame is content generation, namely the definition of maps, terrains, non-player characters (NPCs) and other graphical, musical and AI-related components of the game. Such generation is costly due to its complexity, the great amount of work required and the need of specialized manpower. Hence the relevance of optimizing the process and alleviating costs. In this sense, procedural content generation (PCG) comes in handy as a means of reducing costs by using algorithmic techniques to automatically generate some game contents. PCG also provides advantages in terms of player experience since the contents generated are typically not fixed but can vary in different playing sessions, and can even adapt to the player herself. For this purpose, the underlying algorithmic technique used for PCG must be also flexible and adaptable. This is the case of computational intelligence in general and evolutionary algorithms in particular. In this work we shall provide an overview of the use of evolutionary intelligence for PCG, with special emphasis on its use within the context of realtime strategy games. We shall show how these techniques can address both playability and aesthetics, as well as improving the game AI.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013

ABSTRACT Procedural content generation (PCG) is the programmatic generation of game content using... more ABSTRACT Procedural content generation (PCG) is the programmatic generation of game content using a random or pseudo-random process that results in an unpredictable range of possible gameplay spaces. This methodology brings many advantages to game developers, such as reduced memory consumption. This works presents a procedural balanced map generator for a real-time strategy game: Planet Wars. This generator uses an evolutionary strategy for generating and evolving maps and a tournament system for evaluating the quality of these maps in terms of their balance. We have run several experiments obtaining a set of playable and balanced maps.

Lecture Notes in Computer Science, 2013

Car racing is a successful genre of videogames, as proved, for example, by the racing simulator s... more Car racing is a successful genre of videogames, as proved, for example, by the racing simulator saga, Gran Turismo. In this genre of games, players not only race but they are also involved in the process of setting up the car, assuming the role of a technician/mechanic/engineer. Generally, this configuration deals with a large set of parameters that range from the amount of fuel loaded into the car to the tire pressure and type. This article compares different proposals for optimizing this process using evolutionary computation techniques to make several suggestions for a simulated international competition for car racing setup optimization.

Lecture Notes in Computer Science, 2013

This work describes the design of a bot for the first person shooter Unreal Tournamenta 2004 (UT2... more This work describes the design of a bot for the first person shooter Unreal Tournamenta 2004 (UT2K4), which behaves as a human expert player in 1 vs. 1 death matches. This has been implemented modelling the actions (and tricks) of this player, using a state-based IA, and supplemented by a database for 'learning' the arena. The expert bot yields excellent results, beating the game default bots in the hardest difficulty, and even being a very hard opponent for the human players (including our expert). The AI of this bot is then improved by means of three different approaches of evolutionary algorithms, optimizing a wide set of parameters (weights and probabilities) which the expert bot considers when playing. The result of this process yields an even better rival; however the noisy nature of the fitness function (due to the pseudostochasticity of the battles) makes the evolution slower than usual.

2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI), 2013

Real-time strategy games offer a wide variety of fundamental AI research challenges. Most of thes... more Real-time strategy games offer a wide variety of fundamental AI research challenges. Most of these challenges have applications outside the game domain. This paper provides a review on computational intelligence in real-time strategy games (RTS). It starts with challenges in real-time strategy games, then it reviews different tasks to overcome this challenges. Later, it describes the techniques used to solve this challenges and it makes a relationship between techniques and tasks. Finally, it presents a set of different frameworks used as test-beds for the techniques employed. This paper is intended to be a starting point for future researchers on this topic.

Natural Computing, 2014

We consider search-based procedural content generation in the context of Planet Wars, an RTS game... more We consider search-based procedural content generation in the context of Planet Wars, an RTS game. The objective of this work is to generate maps for the aforementioned game, that result in an interesting game-play. In order to characterize interestingness we focus on the properties of balance and dynamism. The former captures the fact that no player is overwhelmed by the opponent during the game, whereas the latter tries to model the fact that there is a lot of action during the game. To measure these properties on a given map, we conduct several games on them using top AI bots and collect statistics which are, in turn, used as inputs of a fuzzy rule base. This system is embedded within an evolutionary algorithm that features self-adaptation of mutation parameters as well as variablelength chromosomes (thus implying maps of different sizes). The experimentation focuses both on the optimization of balance and dynamism as stand-alone properties and in the analysis of the different tradeoffs attainable through them. To reach this goal a multi objective approach is used. We analyze both the usefulness of map-size self-adaptation in each scenario, as well as the properties of maps leading to different tradeoffs between dynamism and balance.

Games constitute a research domain that is attracting the interest of scientists from numerous di... more Games constitute a research domain that is attracting the interest of scientists from numerous disciplines. This is particularly true from the perspective of computational intelligence. In order to examine the growing importance of this area in the gaming domain, we present an analysis of the scientific collaboration network of researchers working on computational intelligence in games (CIG). This network has been constructed from bibliographical data obtained from the Digital Bibliography & Library Project (DBLP). We have analyzed from a temporal perspective several properties of the CIG network at the macroscopic, mesoscopic and microscopic levels, studying the large-scale structure, the growth mechanics, and collaboration patterns among other features. Overall, computational intelligence in games exhibits similarities with other collaboration networks such as for example a log-normal degree distribution and sub-linear preferential attachment for new authors. It also has distinctive features, e.g. the number of papers co-authored is exponentially distributed, the internal preferential attachment (new collaborations among existing authors) is linear, and fidelity rates (measured as the relative preference for publishing with previous collaborators) grow super-linearly. The macroscopic and mesoscopic evolution of the network indicates the field is very active and vibrant, but it is still at an early developmental stage. We have also analyzed communities and central nodes and how these are reflected in research topics, thus identifying active research subareas.

2014 IEEE Congress on Evolutionary Computation (CEC), 2014

Lecture Notes in Computer Science, 2014

Procedural content generation (PCG) is the programmatic generation of game content using a random... more Procedural content generation (PCG) is the programmatic generation of game content using a random or pseudo-random process that results in an unpredictable range of possible game play spaces. This methodology brings many advantages to game developers, such as reduced memory consumption. In this paper we introduce a procedural map generator for a real-time strategy (RTS) game. The main component of this generator is a genetic algorithm devoted to create and evolve balanced maps, i.e. maps where no player has any map related advantage with respect to other players. The selected RTS game is called Planet Wars and it was used in the Google AI Challenge 2010. It is a space conquest game whose objective is to take over all the planets on the map.

Entertainment Computing, 2014

ABSTRACT This paper presents a procedural content generation (PCG) method that is able to generat... more ABSTRACT This paper presents a procedural content generation (PCG) method that is able to generate aesthetic maps for a real-time strategy game. The maps were characterized based on either their geometrical properties or their topological measures (obtained in this latter case from the sphere-of-influence graph induced by each map). Using these features, a distance function between maps can be defined. This function is used in turn to determine how close/far each map generated by the PCG method (a self-adaptive evolutionary algorithm) is to a collection of maps which were taken initially to be aesthetic or non-aesthetic. This correspondence guided a multi-objective evolutionary approach whereby maps close to aesthetic maps and far to non-aesthetic maps are sought. Self-organizing maps are used to ascertain whether the so-generated maps naturally cluster together with aesthetic maps, as well as to provide a qualitative assessment of the ability of each set of features to characterize the latter.

International Journal of Interactive Multimedia and Artificial Intelligence, 2015

Videogames are one of the most important and profitable sectors in the industry of entertainment.... more Videogames are one of the most important and profitable sectors in the industry of entertainment. Nowadays, the creation of a videogame is often a large-scale endeavor and bears many similarities with, e.g., movie production. On the central tasks in the development of a videogame is content generation, namely the definition of maps, terrains, non-player characters (NPCs) and other graphical, musical and AI-related components of the game. Such generation is costly due to its complexity, the great amount of work required and the need of specialized manpower. Hence the relevance of optimizing the process and alleviating costs. In this sense, procedural content generation (PCG) comes in handy as a means of reducing costs by using algorithmic techniques to automatically generate some game contents. PCG also provides advantages in terms of player experience since the contents generated are typically not fixed but can vary in different playing sessions, and can even adapt to the player herself. For this purpose, the underlying algorithmic technique used for PCG must be also flexible and adaptable. This is the case of computational intelligence in general and evolutionary algorithms in particular. In this work we shall provide an overview of the use of evolutionary intelligence for PCG, with special emphasis on its use within the context of realtime strategy games. We shall show how these techniques can address both playability and aesthetics, as well as improving the game AI.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013

ABSTRACT Procedural content generation (PCG) is the programmatic generation of game content using... more ABSTRACT Procedural content generation (PCG) is the programmatic generation of game content using a random or pseudo-random process that results in an unpredictable range of possible gameplay spaces. This methodology brings many advantages to game developers, such as reduced memory consumption. This works presents a procedural balanced map generator for a real-time strategy game: Planet Wars. This generator uses an evolutionary strategy for generating and evolving maps and a tournament system for evaluating the quality of these maps in terms of their balance. We have run several experiments obtaining a set of playable and balanced maps.

Lecture Notes in Computer Science, 2013

Car racing is a successful genre of videogames, as proved, for example, by the racing simulator s... more Car racing is a successful genre of videogames, as proved, for example, by the racing simulator saga, Gran Turismo. In this genre of games, players not only race but they are also involved in the process of setting up the car, assuming the role of a technician/mechanic/engineer. Generally, this configuration deals with a large set of parameters that range from the amount of fuel loaded into the car to the tire pressure and type. This article compares different proposals for optimizing this process using evolutionary computation techniques to make several suggestions for a simulated international competition for car racing setup optimization.

Lecture Notes in Computer Science, 2013

This work describes the design of a bot for the first person shooter Unreal Tournamenta 2004 (UT2... more This work describes the design of a bot for the first person shooter Unreal Tournamenta 2004 (UT2K4), which behaves as a human expert player in 1 vs. 1 death matches. This has been implemented modelling the actions (and tricks) of this player, using a state-based IA, and supplemented by a database for 'learning' the arena. The expert bot yields excellent results, beating the game default bots in the hardest difficulty, and even being a very hard opponent for the human players (including our expert). The AI of this bot is then improved by means of three different approaches of evolutionary algorithms, optimizing a wide set of parameters (weights and probabilities) which the expert bot considers when playing. The result of this process yields an even better rival; however the noisy nature of the fitness function (due to the pseudostochasticity of the battles) makes the evolution slower than usual.

2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI), 2013

Real-time strategy games offer a wide variety of fundamental AI research challenges. Most of thes... more Real-time strategy games offer a wide variety of fundamental AI research challenges. Most of these challenges have applications outside the game domain. This paper provides a review on computational intelligence in real-time strategy games (RTS). It starts with challenges in real-time strategy games, then it reviews different tasks to overcome this challenges. Later, it describes the techniques used to solve this challenges and it makes a relationship between techniques and tasks. Finally, it presents a set of different frameworks used as test-beds for the techniques employed. This paper is intended to be a starting point for future researchers on this topic.

Natural Computing, 2014

We consider search-based procedural content generation in the context of Planet Wars, an RTS game... more We consider search-based procedural content generation in the context of Planet Wars, an RTS game. The objective of this work is to generate maps for the aforementioned game, that result in an interesting game-play. In order to characterize interestingness we focus on the properties of balance and dynamism. The former captures the fact that no player is overwhelmed by the opponent during the game, whereas the latter tries to model the fact that there is a lot of action during the game. To measure these properties on a given map, we conduct several games on them using top AI bots and collect statistics which are, in turn, used as inputs of a fuzzy rule base. This system is embedded within an evolutionary algorithm that features self-adaptation of mutation parameters as well as variablelength chromosomes (thus implying maps of different sizes). The experimentation focuses both on the optimization of balance and dynamism as stand-alone properties and in the analysis of the different tradeoffs attainable through them. To reach this goal a multi objective approach is used. We analyze both the usefulness of map-size self-adaptation in each scenario, as well as the properties of maps leading to different tradeoffs between dynamism and balance.

Games constitute a research domain that is attracting the interest of scientists from numerous di... more Games constitute a research domain that is attracting the interest of scientists from numerous disciplines. This is particularly true from the perspective of computational intelligence. In order to examine the growing importance of this area in the gaming domain, we present an analysis of the scientific collaboration network of researchers working on computational intelligence in games (CIG). This network has been constructed from bibliographical data obtained from the Digital Bibliography & Library Project (DBLP). We have analyzed from a temporal perspective several properties of the CIG network at the macroscopic, mesoscopic and microscopic levels, studying the large-scale structure, the growth mechanics, and collaboration patterns among other features. Overall, computational intelligence in games exhibits similarities with other collaboration networks such as for example a log-normal degree distribution and sub-linear preferential attachment for new authors. It also has distinctive features, e.g. the number of papers co-authored is exponentially distributed, the internal preferential attachment (new collaborations among existing authors) is linear, and fidelity rates (measured as the relative preference for publishing with previous collaborators) grow super-linearly. The macroscopic and mesoscopic evolution of the network indicates the field is very active and vibrant, but it is still at an early developmental stage. We have also analyzed communities and central nodes and how these are reflected in research topics, thus identifying active research subareas.