Evolutionary Game Design

2008

This paper presents a first attempt at evolving the rules for a game. In contrast to almost every other paper that applies computational intelligence techniques to games, we are not generating behaviours, strategies or environments for any particular game; we are starting without a game and generating the game itself. We explain the rationale for doing this and survey the theories of entertainment and curiosity that underly our fitness function, and present the details of a simple proofof-concept experiment.

Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developer's task. In this work we present an evolutionary strategy based solution towards the automatic generation of two player board games. To guide the evolutionary process towards games, which are entertaining, we propose a set of metrics. These metrics are based upon different theories of entertainment in computer games. This work also compares the entertainment value of the evolved games with the existing popular board based games. Further to verify the entertainment value of the evolved games with the entertainment value of the human user a human user survey is conducted. In addition to the user survey we check the learnability of the evolved games using an artificial neural network based controller. The proposed metrics and the evolutionary process can be employed for generating new and entertaining board games, provided an initial search space is given to the evolutionary algorithm.

With computers becoming ubiquitous and high resolution graphics reaching the next level, computer games have become a major source of entertainment. It has been a tedious task for game developers to measure the entertainment value of the computer games. The entertainment value of a game does depend upon the genre of the game in addition to the game contents. In this paper, we propose a set of entertainment metrics for the platform genre of games. The set of entertainment metrics is proposed based upon certain theories on entertainment in computer games. To test the metrics, we use an evolutionary algorithm for automated generation of game rules which are entertaining. The proposed approach starts with an initial set of randomly generated games and, based upon the proposed metrics as an objective function, guides the evolutionary process. The results produced are counterchecked against the entertainment criteria of humans by conducting a human user survey and a controller learning ability experiment. The proposed metrics and the evolutionary process of generating games can be employed by any platform game for the purpose of automatic generation of interesting games provided an initial search space is given.

2013

Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developer's task. In this work we present an evolutionary strategy based solution towards the automatic generation of two player board games. To guide the evolutionary process towards games, which are entertaining, we propose a set of metrics. These metrics are based upon different theories of entertainment in computer games. This work also compares the entertainment value of the evolved games with the existing popular board based games. Further to verify the entertainment value of the evolved games with the entertainment value of the human user a human user survey is conducted. In addition to the user survey we check the learnability of the evolved games using an artificial neural network based controller. The proposed metrics and the evolutionary process can be employed for ge...

2017

The production of video games is a complex process, which involves several disciplines, spanning from art to computer science. The final goal is to keep entertained the players, by continuously providing them novel and challenging contents. However, the availability of a large variety of pre-produced material is often not possible. A similar problem can be found in many single-player game genres, where the simulated behaviour generated by the Artificial Intelligence algorithms must be coherent, believable, but also adequately variegate to maintain a satisfactory user experience. To this aim, there is a growing interest in the introduction of automatic or semi-automatic techniques to produce and manage the video game contents. In this paper, we present an example of strategic card battle video game based on the applications of Artificial Intelligence and Genetic Algorithms, where the game contents are dynamically adapted and produced during the game sessions. ACM Classification

International Journal of Information Technology, Communications and Convergence, 2010

Over the period of time computer games have became a major source of entertainment for humans. From the point of view of game developers there is a constant demand of writing games which are entertaining for the end users but entertainment itself is of subjective nature. It has always been difficult to quantify the entertainment value of the human player. The two factors which mainly influence the entertainment value are the type of the game and the contents of the game. In this paper we address the issues of measuring entertainment and automatic generation of computer games. We present some quantitative measures for entertainment in a genre of computer game and apply them as a guide for the evolution of new interesting games.

2012 IEEE International Games Innovation Conference, 2012

Setting parameters for video games is important and often difficult. Poor choices can lead to games being too easy or too difficult, too short or too long, too linear or too open-ended. In many cases there is no alternative to making value judgments on what these parameter values should be. This paper explores whether evolutionary computation can be successfully applied to finding values that facilitate desired gameplay objectives in a Tower Defense game. Initial experiments suggest that a two-tier evolutionary method can create strategies to successfully play such games and configurations of game parameters that facilitate gameplay objectives desired by the developer.

2018

The usage of player modeling in games is constantly growing and being studied nowadays. It is a way of connecting the user to the game in a personal way, since the game will have different results and obstacles based on the user playstyle. Generally, games that use player modeling comes with a confirmation from the user to proceed with the changes in the game. Our proposed model is an attempt to make the game even more personal. We built a prototype that does not ask the player for guidance, the game autonomously change and shape the player characters by the way he/she acts in the game. The developed prototype is a species evolution game, where the player chooses one species from four available. This species is only controlled by the player, while the others are autonomously controlled by AI agents. The objective is to evolve and survive in the game, since there are some threats to the user like species combat and starvation, for instance. The results of this prototype was satisfact...

2010

Abstract. A generative system that creates levels for 2D platformer games is presented. The creation process is driven by generic models of challenge-based fun which are derived from existing theories of game design. These models are used as fitness functions in a genetic algorithm to produce new levels that maximize the amount of player fun, and the results are compared with existing levels from the classic video game Super Mario Bros.

Neural Computing and Applications, 2020

Real-time strategy (RTS) games differ as they persist in varying scenarios and states. These games enable an integrated correspondence of non-player characters (NPCs) to appear as an autodidact in a dynamic environment, thereby resulting in a combined attack of NPCs on human-controlled character (HCC) with maximal damage. This research aims to empower NPCs with intelligent traits. Therefore, we instigate an assortment of ant colony optimization (ACO) with genetic algorithm (GA)-based approach to first-person shooter (FPS) game, i.e., Zombies Redemption (ZR). Eminent NPCs with bestfit genes are elected to spawn NPCs over generations and game levels as yielded by GA. Moreover, NPCs empower ACO to elect an optimal path with diverse incentives and less likelihood of getting shot. The proposed technique ZR is novel as it integrates ACO and GA in FPS games where NPC will use ACO to exploit and optimize its current strategy. GA will be used to share and explore strategy among NPCs. Moreover, it involves an elaboration of the mechanism of evolution through parameter utilization and updation over the generations. ZR is played by 450 players with varying levels having the evolving traits of NPCs and environmental constraints in order to accumulate experimental results. Results revealed improvement in NPCs performance as the game proceeds.