Automated tweaking of levels for casual creation of mobile games

Casual creator software lowers the technical barriers to creative expression. Although casual creation of visual art, music, text and game levels is well established, few casual creators allow users to create entire games: despite many tools that aim to make the process easier, development of a game from start to finish still requires no small amount of technical ability. We are developing an iOS app called Gamika which seeks to change this, mainly through the use of AI and computational creativity techniques to remove some of the technical and creative burden from the user. In this paper we describe an initial step towards this: a Gamika component that takes a level designed by the user, and tweaks its parameters to improve its playability. The AI techniques used are straightforward: rule-based automated playtesting, random search, and decision trees learning. While there is room for improvement, as a proof of concept for this kind of mixed-initiative creation, the system already shows great promise

Semi-automated level design via auto-playtesting for handheld casual game creation

We provide a proof of principle that novel and engaging mobile casual games with new aesthetics, game mechanics and player interactions can be designed and tested directly on the device for which they are intended. We describe the Gamika iOS application which includes generative art assets; a design interface enabling the making of physics-based casual games containing multiple levels with aspects ranging from Frogger-like to Asteroids-like and beyond; a configurable automated playtester which can give feedback on the playability of levels; and an automated fine-tuning engine which searches for level parameterisations that enable the game to pass a battery of tests, as evaluated by the auto-playtester. Each aspect of the implementation represents a baseline with much room for improvement, and we present some experimental results and describe how these will guide the future directions for Gamika

Algorithmic modifications in procedural generation systems

The Modified Diamond Square algorithm is presented, as a result of which a 3-dimensional map of the fractal surface is obtained. This method of visualization using voxels allows to generate the relief structures (caves, canyons, quarries) that cannot be generated using a regular elevation map. The result of using the modified algorithm is shown on the landscape construction.Мета статті — представлення модифікованого алгоритму Diamond Square, в результаті роботи якого можна отримати тривимірну карту фрактальної поверхні. Методи. У середовищі Unity була створена демонстрація для побудови ігрових рівнів, яка дозволяє будувати карти на основі модифікованого алгоритму Diamond Square. В результаті своєї роботи отримаємо 3-мірну карту фрактальної поверхні, яка може бути використана у майбутніх проектах. Вихідна програма служить для демонстрації роботи алгоритму і ґрунтується на трьох сценаріях: модифікованому алгоритмі Diamond Square, допоміжних алгоритмах обробки і сценаріїв камери. Результати. В статті представлено модифікований алгоритм Diamond Square, в результаті роботи якого отримуємо тривимірну карту фрактальної поверхні. Результат використання модифікованого алгоритму показано на прикладі побудови ландшафтної конструкції.Цель статьи — представление модифицированного алгоритма Diamond Square, в результате работы которого можно получить трехмерную карту фрактальной поверхности. Методы. В среде Unity была создана демонстрация для построения игровых уровней, которая позволяет строить карты на основе модифицированного алгоритма Diamond Square. В результате работы алгоритма получается 3-мерная карта фрактальной поверхности, которая может быть использована в будущих проектах. Исходная программа служит для демонстрации работы алгоритма и основывается на трех сценариях: модифицированном алгоритме Diamond Square, вспомогательных алгоритмах обработки и сценариев камеры. Результаты. В статье представлен модифицированный алгоритм Diamond Square, в результате работы которого получаем трехмерную карту фрактальной поверхности. Результат использования модифицированного алгоритма показан на примере построения ландшафтной конструкции

Languages of games and play: A systematic mapping study

Digital games are a powerful means for creating enticing, beautiful, educational, and often highly addictive interactive experiences that impact the lives of billions of players worldwide. We explore what informs the design and construction of good games to learn how to speed-up game development. In particular, we study to what extent languages, notations, patterns, and tools, can offer experts theoretical foundations, systematic techniques, and practical solutions they need to raise their productivity and improve the quality of games and play. Despite the growing number of publications on this topic there is currently no overview describing the state-of-the-art that relates research areas, goals, and applications. As a result, efforts and successes are often one-off, lessons learned go overlooked, language reuse remains minimal, and opportunities for collaboration and synergy are lost. We present a systematic map that identifies relevant publications and gives an overview of research areas and publication venues. In addition, we categorize research perspectives along common objectives, techniques, and approaches, illustrated by summaries of selected languages. Finally, we distill challenges and opportunities for future research and development

Automating Game-design and Game-agent Balancing through Computational Intelligence

Game design has been a staple of human ingenuity and innovation for as long as games have been around. From sports, such as football, to applying game mechanics to the real world, such as reward schemes in shops, games have impacted the world in surprising ways. The process of developing games can, and should, be aided by automated systems, as machines have proven capable of finding innovative ways of complementing human intuition and inventiveness. When man and machine co-operate, better products are created and the world has only to benefit. This research seeks to find, test and assess methods of using genetic algorithms to human-led game balancing tasks. From tweaking difficulty to optimising pacing, to directing an intelligent agent’s behaviour, all these can benefit from an evolutionary approach and save a game designer many hours, if not days, of work based on trial and error. Furthermore, to improve the speed of any developed GAs, predictive models have been designed to aid the evolutionary process in finding better solutions faster. While these techniques could be applied on a wider variety of tasks, they have been tested almost exclusively on game balance problems. The major contributions are in defining the main challenges of game balance from an academic perspective, proposing solutions for better cooperation between the academic and the industrial side of games, as well as technical improvements to genetic algorithms applied to these tasks. Results have been positive, with success found in both academic publications and industrial cooperation

A Network Theory of Patentability

Patent law is built upon a fundamental premise: only significant inventions receive patent protection while minor improvements remain in the public domain. This premise is indispensable for maintaining an optimal balance between incentivizing new innovation and providing public access to existing innovation. Despite its importance, the doctrine that performs this gatekeeping role—nonobviousness— has long remained indeterminate and vague. Judicial opinions have struggled to articulate both what makes an invention significant (or nonobvious) and how to measure nonobviousness in specific cases. These difficulties are due in large part to the existence of two clashing theoretical frameworks, cognitive and economic, that have vied for prominence in justifying nonobviousness. Neither framework, however, has generated doctrinal tests that can be easily and consistently applied. This Article draws on a novel approach—network theory—to answer both the conceptual question (what is a nonobvious invention?) and the measurement question (how do we determine nonobviousness in specific cases?). First, it shows that what is missing in current conceptual definitions of nonobviousness is an underlying theory of innovation. It then supplies this missing piece. Building upon insights from network science, we model innovation as a process of search and recombination of existing knowledge. Distant searches that combine disparate or weakly connected portions of social and information networks tend to produce high-impact, new ideas that open novel innovation trajectories. Distant searches also tend to be costly and risky. In contrast, local searches tend to result in incremental innovation that is more routine, less costly, and less risky. From a network theory perspective, then, the goal of nonobviousness should be to reward, and therefore to incentivize, those risky distant searches and recombinations that produce the most socially significant innovations. By emphasizing factors specific to the structure of innovation—namely, the risks and costs of the search and recombination process—a network approach complements and deepens current economic understandings of nonobviousness. Second, based on our network theory of innovation, we develop an empirical, algorithmic measure of patentability—what we term a patent’s “network nonobviousness score” (NNOS). We harness data from US patent records to calculate the distance between the technical knowledge areas recombined in any given invention (or patent), allowing us to assign each patent a specific NNOS. We propose a doctrinal framework that incorporates an invention’s NNOS to nonobviousness determinations both at the examination phase and during patent litigation. Our use of network science to develop a legal algorithm is a methodological innovation in law, with implications for broader debates about computational law. We illustrate how differences in algorithm design can lead to different nonobviousness outcomes, and discuss how to mitigate the negative impact of black box algorithms

Automatic Game Parameter Tuning using General Video Game Agents

Automatic Game Design is a subfield of Game Artificial Intelligence that aims to study the usage of AI algorithms for assisting in game design tasks. This dissertation presents a research work in this field, focusing on applying an evolutionary algorithm to video game parameterization. The task we are interested in is player experience. N-Tuple Bandit Evolutionary Algorithm (NTBEA) is an evolutionary algorithm that was recently proposed and successfully applied in game parameterization in a simple domain, which is the first experiment included in this project. To further investigating its ability in evolving game parameters, We applied NTBEA to evolve parameter sets for three General Video Game AI (GVGAI) games, because GVGAI has variety supplies of video games in different types and the framework has already been prepared for parameterization. 9 positive increasing functions were picked as target functions as representations of the player expected score trends. Our initial assumption was that the evolved games should provide the game environments that allow players to obtain score in the same trend as one of these functions. The experiment results confirm this for some functions, and prove that the NTBEA is very much capable of evolving GVGAI games to satisfy this task