Procedural Content Generation Research Articles

A modern approach to design and integrate conceptual methods in video games with artificial intelligence

Working professionals life in metropolitan city seems to be worst with full of stress and anxiety. With hectic schedule they don’t even have time for their family. In order to overcome stress and to even get entertained and to relax they can consider games as an important medium. To remove boredom from life and to make sure that life is interesting and exciting they should be entertained. After heavy work schedules no one will effort to go out to get relaxed. There are many ways through which people can get entertained. We are going to focus on one of the methods of entertainment that is through video games. Video games have got many pros and cons generally many people will focus on the cons like continuous playing of games will lead to health problems like problem in eye sight apart from the cons there are pros like attaining success in professional life because these games it helps in making faster decisions since next move has to be made immediately as every game has to be played within limited span of time. Video games also helps in career boost like understanding leadership qualities and performing any task by following strategies through which goals can be attained with in stipulated time. Video game AI (artificial intelligence) is inspired by the human ability to identify the correlation between the games and applying same behavior on conceptual level. Human behavior varies based on the conceptual view and actions. For any video game there should be any medium through which human being can interact like graphical user interface, scripting API and plug-in API. The programming model that is followed is based on time simulation. There are many other challenges faced in the artificial intelligence or computational intelligence techniques while creating and designing video games. Problem arises when a human plays with an entity that performs human like actions. In which decisions are not controlled by human player. In this paper we try to explore how to design conceptual AI and how to integrate conceptual AI in video games and discuss about interfaces like graphical user interface, scripting API, plug-in API and challenges faced like game artificial intelligence,non-player characters, procedural content generation,affective computing,human-like behavior.

Search -based Procedural Content Generation for Race Tracks in Video Games

Search -based Procedural Content Generation for Race Tracks in Video Games

Globalized random procedural content for dungeon generation

Procedural content generation has been a major part of computer gaming since the early 1980s. Because of the limited memory in early computers and video game consoles, procedural generation was use...

Competitive Algorithms for Coevolving Both Game Content and AI. A Case Study: <italic>Planet Wars</italic>

The classical approach of competitive coevolution (CC) applied in games tries to exploit an arms race between coevolving populations that belong to the same species (or at least to the same biotic niche), namely strategies, rules, tracks for racing, or any other. This paper proposes the coevolution of entities belonging to different realms (namely biotic and abiotic) via a competitive approach. More precisely, we aim to coevolutionarily optimize both virtual players and game content. From a general perspective, our proposal can be viewed as a method of procedural content generation combined with a technique for generating game AI. This approach can not only help game designers in game creation but also generate content personalized to both specific players’ profiles and game designer’s objectives (e.g., create content that favors novice players over skillful players). As a case study we use Planet Wars , the real-time strategy (RTS) game associated with the 2010 Google AI Challenge contest, and demonstrate (via an empirical study) the validity of our approach.

Petalz: Search-Based Procedural Content Generation for the Casual Gamer

The impact of game content on the player experience is potentially more critical in casual games than in competitive games because of the diminished role of strategic or tactical diversions. Interestingly, until now procedural content generation (PCG) has nevertheless been investigated almost exclusively in the context of competitive, skills-based gaming. This paper therefore opens a new direction for PCG by placing it at the center of an entirely casual flower-breeding game platform called Petalz. That way, the behavior of players and their reactions to different game mechanics in a casual environment driven by PCG can be investigated. In particular, players in Petalz can: 1) trade their discoveries in a global marketplace; 2) respond to an incentive system that awards diversity; and 3) generate real-world 3-D replicas of their evolved flowers. With over 1900 registered online users and 38 646 unique evolved flowers, Petalz showcases the potential for PCG to enable these kinds of casual game mechanics, thus paving the way for continued innovation with PCG in casual gaming.

Procedural content generation for platformers: designing and testing FUN PLEdGE

Video games are a peculiar medium, standing at the crossing point between art and software application, and characterized by an active involvement of its audience. The complexity of the product generates a huge challenge for the companies that develop video games. In the development process, level designers play a crucial role: they are in charge of declining the theoretical framework developed by the game designer into game levels, which contain the actual gameplay scenarios. Hence, the final goal of any level designer is to valorize the game design by creating enjoyable gaming experiences while, at the same time, respecting several constraints. To lighten the burden on level designers, several semi-automated approaches to level generation have appeared in time, but the majority of these tools suffer from several drawbacks. In the present work, we tackle the issue of designing, prototyping and testing FUN PLEdGE, a general-purpose automated levels generator and editor for platform video games. Its main goal is to shrink development time while producing --- unassisted --- levels that are both playable and fun. Moreover, our tool provides the maximum freedom to the level designer, by avoiding to impose unnecessary constraints on the structure of the levels and by guaranteeing the possibility to modify and personalize by hand the generated levels. During this process, the generator assists the designer by suggesting corrections functional to the quality of the player experience. To prove the effectiveness of our prototypal application we have also developed and tested with players a platform game. In the same vein, we asked to a group of game developers to test FUN PLEdGE.

Feminism and procedural content generation: toward a collaborative politics of computational creativity

ABSTRACTGames now inhabit a space where creativity is no longer centered around human authorship. The use of procedural content generation has been embraced by industry, academics and fans as a means for reducing labor cost, providing additional replayable content for players, investigating computational creativity in a complex and multifaceted domain and enabling new kinds of playable experiences. This incorporation of computational creative labor confuses authorship, labor politics and responsibility for rhetoric embedded in the procedures by complicating the way in which the computer is portrayed to users, researchers and other developers. We can apply feminist methodologies attentive to questions of difference and power in systemic structures in order to better understand each of these questions in turn. This article presents an analysis of the post-anthropocentric phenomenon of computer creativity within games, via a feminist analysis of procedural content generating algorithms, its role in game design and its public portrayal.

A Panorama of Artificial and Computational Intelligence in Games

This paper attempts to give a high-level overview of the field of artificial and computational intelligence (AI/CI) in games, with particular reference to how the different core research areas within this field inform and interact with each other, both actually and potentially. We identify ten main research areas within this field: NPC behavior learning, search and planning, player modeling, games as AI benchmarks, procedural content generation, computational narrative, believable agents, AI-assisted game design, general game artificial intelligence and AI in commercial games. We view and analyze the areas from three key perspectives: 1) the dominant AI method(s) used under each area; 2) the relation of each area with respect to the end (human) user; and 3) the placement of each area within a human–computer (player-game) interaction perspective. In addition, for each of these areas we consider how it could inform or interact with each of the other areas; in those cases where we find that meaningful interaction either exists or is possible, we describe the character of that interaction and provide references to published studies, if any. We believe that this paper improves understanding of the current nature of the game AI/CI research field and the interdependences between its core areas by providing a unifying overview. We also believe that the discussion of potential interactions between research areas provides a pointer to many interesting future research projects and unexplored subfields.

Integrated Approach to Personalized Procedural Map Generation Using Evolutionary Algorithms

In this paper, we propose the strategy of integrating multiple evolutionary processes for personalized procedural content generation (PCG). In this vein, we provide a concrete solution that personalizes game maps in a top-down action-shooter game to suit an individual player's preferences. The need for personalized PCG is steadily growing as the player market diversifies, making it more difficult to design a game that will accommodate a broad range of preferences and skills. In the solution presented here, the geometry of the map and the density of content within that geometry are represented and generated in distinct evolutionary processes, with the player's preferences being captured and utilized through a combination of interactive evolution and a player model formulated as a recommender system. All these components were implemented into a test bed game and experimented on through an unsupervised public experiment. The solution is examined against a plausible random baseline that is comparable to random map generators that have been implemented by independent game developers. Results indicate that the system as a whole is receiving better ratings, that the geometry and content evolutionary processes are exploring more of the solution space, and that the mean prediction accuracy of the player preference models is equivalent to that of existing recommender system literature. Furthermore, we discuss how each of the individual solutions can be used with other game genres and content types.

SAT Modulo Monotonic Theories

Boolean satisfiability (SAT) solvers have been successfully applied to a wide variety of difficult combinatorial problems. Many further problems can be solved by SAT Modulo Theory (SMT) solvers, which extend SAT solvers to handle additional types of constraints. However, building efficient SMT solvers is often very difficult. In this paper, we define the concept of a Boolean monotonic theory and show how to easily build efficient SMT solvers, including effective theory propagation and clause learning, for such theories. We present examples showing useful constraints that are monotonic, including many graph properties (e.g., shortest paths), and geometric properties (e.g., convex hulls). These constraints arise in problems that are otherwise difficult for SAT solvers to handle, such as procedural content generation. We have implemented several monotonic theory solvers using the techniques we present in this paper and applied these to content generation problems, demonstrating major speed-ups over SAT, SMT, and Answer Set Programming solvers, easily solving instances that were previously out of reach.

Learning-Based Procedural Content Generation

Procedural content generation (PCG) has recently become one of the hottest topics in computational intelligence and AI game research. While some substantial progress has been made in this area, there are still several challenges ranging from content evaluation to personalized content generation. In this paper, we present a novel PCG framework based on machine learning, named learning-based procedure content generation (LBPCG), to tackle a number of challenging problems. By exploring and exploiting information gained in game development and public player test, our framework can generate robust content adaptable to end-user or target players on-line with minimal interruption to their gameplay experience. As the data-driven methodology is emphasized in our framework, we develop learning-based enabling techniques to implement the various models required in our framework. For a proof of concept, we have developed a prototype based on the classic open source first-person shooter game, Quake. Simulation results suggest that our framework is promising in generating quality content.

Обзор систем процедурной генерации игр

Procedural content generation (PCG) is one of the most important fields of research in videogame industry. PCG allows creating different parts of games automatically; an interesting task of PCG is generating game rules and even whole games. In this paper, we present an overview of research in this field; algorithms from evolutionary computation and logic programming fields that can be used to generate game rules and games in different genres are described, as well as methods of evaluating the resulting games. We briefly describe general game-playing programs and the PCG systems that were used in them. Several representations and description languages utilized in PCG systems to encode game rules are also described.

Procedural Content Generation for Real-Time Strategy Games

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 real- time strategy games. We shall show how these techniques can address both playability and aesthetics, as well as improving the game AI. PURRED on by the emergence of the videogame industry as the main component of the entertainment industry has motivated, research on videogames has acquired increasing notoriety during the last years. Such research spans many areas such as marketing and gamification, psychology and player satisfaction, computational intelligence, education and health (serious games) and computer graphics, just to cite a few. This diversification of research areas is largely motivated by a shift in the priorities of the video game industry: while games used to rely heavily on their graphical quality, other features such as the music, the player immersion into the game and interesting storyline have gained enormous importance. To cope with the plethora of new interesting challenges in the area of

TrackGen: An interactive track generator for TORCS and Speed-Dreams

TrackGen is an online tool for the generation of tracks for two open-source 3D car racing games (TORCS and Speed Dreams). It integrates interactive evolution with procedural content generation and comprises two components: (i) a web frontend that maintains the database of all the evolved populations and manages the interaction with users (by collecting users evaluations and providing access to the evolved tracks) and (ii) an evolutionary/content-generation backend that runs both the evolutionary algorithm and generates the actual game content that is available through the web frontend. The first prototype of the tool was presented in July 2011 but advertised only to researchers; the first official version which generated tracks only for TORCS was released to the game community in September 2011; due to the many requests, we released a new version soon afterwards, in January 2012, with support for Speed Dreams (the fork of TORCS focused on visual realism and graphic quality) that has been online since then. From January 2012 until July 2014, TrackGen had more than 7600 unique visitors who visited the website around 11,500 times and viewed 85,500 pages; it was employed to evolve more than 8853 tracks, and it was used to download 12,218 tracks. Some of the tracks evolve by our system have been also included in the TORCS distribution.

Geometrical vs topological measures for the evolution of aesthetic maps in a RTS game

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.

The Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE): A Report

The Ninth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) was held October 14–18, 2013, at Northeastern University in Boston, Massachusetts. The mission of the AIIDE conference is to provide a forum for researchers and game developers to discuss ways that AI can enhance games and other forms of interactive entertainment. In addition to presentations on adapting standard AI techniques such as search, planning, and machine learning for use within games, key topic areas include creating realistic autonomous characters, interactive narrative, procedural content generation, and integrating AI into game design and production tools.

On balance and dynamism in procedural content generation with self-adaptive evolutionary algorithms

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 variable-length 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.

Procedural Generation of Dungeons

The use of procedural content generation (PCG) techniques in game development has been mostly restricted to very specific types of game elements. PCG has seldom been deployed for generating entire game levels, a notable exception to this being dungeons: a specific type of game level often encountered in adventure and role playing games. Due to their peculiar combination of pace, gameplay, and game spaces, dungeon levels are among the most suited to showcase the benefits of PCG. This paper surveys research on procedural methods to generate dungeon game levels. We summarize common practices, discuss pros and cons of different approaches, and identify a few promising challenges ahead. In general, what current procedural dungeon generation methods are missing is not performance, but more powerful, accurate, and richer control over the generation process. Recent research results seem to indicate that gameplay-related criteria can provide this high-level control. However, this area is still in its infancy, and many research challenges still lie ahead, e.g., improving the intuitiveness and accessibility of such methods for designers. We also observe that more research is needed into generic mechanisms for automating the generation of the actual dungeon-geometric models. We conclude that the foundations for enabling gameplay-based control of dungeon-level generation are worth being researched, and that its promising results may be instrumental in bringing PCG into mainstream game development.

Разработка системы динамического изменения контента видеоигр на основе эволюционного моделирования

The focus of this research is applying evolutionary algorithms to automatically generating and dynamically changing multimedia data within the context of procedural content generation (PCG) for videogames. Evolutionary computing allows personalizing and adapting the generated content, which is an increasingly important issue in videogame industry. In this paper, we describe existing evolutionary-based PCG systems and propose a universal method for videogame characters representation in such systems. An evolutionary PCG system based on this method was implemented as a part of this research. The system is universal and is well suited for integration in commercial and scientific game projects.

Procedural content generation for games

Hundreds of millions of people play computer games every day. For them, game content—from 3D objects to abstract puzzles—plays a major entertainment role. Manual labor has so far ensured that the quality and quantity of game content matched the demands of the playing community, but is facing new scalability challenges due to the exponential growth over the last decade of both the gamer population and the production costs. Procedural Content Generation for Games (PCG-G) may address these challenges by automating, or aiding in, game content generation. PCG-G is difficult, since the generator has to create the content, satisfy constraints imposed by the artist, and return interesting instances for gamers. Despite a large body of research focusing on PCG-G, particularly over the past decade, ours is the first comprehensive survey of the field of PCG-G. We first introduce a comprehensive, six-layered taxonomy of game content: bits, space, systems, scenarios, design, and derived. Second, we survey the methods used across the whole field of PCG-G from a large research body. Third, we map PCG-G methods to game content layers; it turns out that many of the methods used to generate game content from one layer can be used to generate content from another. We also survey the use of methods in practice, that is, in commercial or prototype games. Fourth and last, we discuss several directions for future research in PCG-G, which we believe deserve close attention in the near future.