RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Although game designers intend to adjust the difficulty of game content, this is a repetitive, labor-intensive, and challenging process, especially for commercial-level games with extensive content. To address this issue, the game research community has explored automated game balancing using artificial intelligence (AI) techniques. However, previous studies have focused on limited game content and did not consider the importance of the generalization ability of playtesting agents when encountering content changes. In this study, we propose RaidEnv, a new game simulator that includes diverse and customizable content for the boss raid scenario in MMORPG games. Additionally, we design two benchmarks for the boss raid scenario that can aid in the practical application of game AI. These benchmarks address two open problems in automatic content balancing, and we introduce two evaluation metrics to provide guidance for AI in automatic content balancing. This novel game research platform expands the frontiers of automatic game balancing problems and offers a framework within a realistic game production pipeline.Comment: 14 pages, 6 figures, 6 tables, 2 algorithm

Game Development Software Engineering Process Life Cycle: A Systematic Review

Software game is a kind of application that is used not only for entertainment, but also for serious purposes that can be applicable to different domains such as education, business, and health care. Multidisciplinary nature of the game development processes that combine sound, art, control systems, artificial intelligence (AI), and human factors, makes the software game development practice different from traditional software development. However, the underline software engineering techniques help game development to achieve maintainability, flexibility, lower effort and cost, and better design. The purpose of this study is to assesses the state of the art research on the game development software engineering process and highlight areas that need further consideration by researchers. In the study, we used a systematic literature review methodology based on well-known digital libraries. The largest number of studies have been reported in the production phase of the game development software engineering process life cycle, followed by the pre-production phase. By contrast, the post-production phase has received much less research activity than the pre-production and production phases. The results of this study suggest that the game development software engineering process has many aspects that need further attention from researchers; that especially includes the post-production phase

Improving Generalization in Game Agents with Data Augmentation in Imitation Learning

Imitation learning is an effective approach for training game-playing agents and, consequently, for efficient game production. However, generalization - the ability to perform well in related but unseen scenarios - is an essential requirement that remains an unsolved challenge for game AI. Generalization is difficult for imitation learning agents because it requires the algorithm to take meaningful actions outside of the training distribution. In this paper we propose a solution to this challenge. Inspired by the success of data augmentation in supervised learning, we augment the training data so the distribution of states and actions in the dataset better represents the real state-action distribution. This study evaluates methods for combining and applying data augmentations to observations, to improve generalization of imitation learning agents. It also provides a performance benchmark of these augmentations across several 3D environments. These results demonstrate that data augmentation is a promising framework for improving generalization in imitation learning agents.Comment: 8 pages, 5 figure

AI in Journalism: Creating an Ethical Framework

This thesis is an examination of the ethical use of artificial intelligence in journalism. Artificial intelligence is currently being used in all steps of the news production process: story discovery, story production and story distribution. Newsrooms utilize machine learning to analyze massive quantities of data and discover patterns that humans would normally never be able to pick up. Additionally, journalists also create templates so computers can write stories that are data-based, such as earning reports and game (sports) stories, and free them up to be able to work on other projects. Newsrooms can also use AI to personalize story recommendations to readers. While there is great potential for machine learning and AI in journalism, it is also an emerging technology that creates new ethical challenges for newsrooms. Interviews conducted with 12 people working in journalism, technology and law focus on issues of bias, transparency, legislation and attribution for algorithms, among others. Based off this research, an ethical framework was built for newsrooms to follow as they implement this technology

Penerapan Augmented reality Berbasis Minimax Algorithm pada Game Papan Cerdas

Abstract. Application of Augmented reality Based on Minimax-Alpha Beta Pruning Algorithm on Smart Board Games. Augmented reality technology is growing very rapidly making game production more innovative and attractive. The implementation of this technology also has the potential for traditional board games which are starting to be replaced by computer-based digital games. The method used in the digital board is Minimax which is zero-sum based where one point of the opponent's victory will reduce the player's one point. This method underlies the way of thinking to get critical steps in several types of games being played. Minimax will result in a lower probability of defeat and increase the probability of winning. The results obtained are that Minimax which was developed with Alpha Beta Pruning to make opponents think like humans so that artificial intelligence in it is suitable to be applied. The test results also give a 63% win for the AI (Artificial Intelligence) used, so the game becomes challenging.Keywords: Game, augmented reality, Minimax, Board Abstrak. Teknologi Augmented reality yang berkembang sangat pesat membuat produksi game lebih inovatif dan atraktif. Implementasi teknologi tersebut juga berpotensi untuk permainan papan tradisional yang mulai tergantikan oleh permainan digital berbasis komputer. Metode yang digunakan dalam digital board adalah Minimax yang berbasis zero-sum dimana satu poin kemenangan lawan akan mengurangi satu poin pemain. Metode ini mendasari cara berfikir untuk mendapatkan langkah-langkah kritis dalam beberapa jenis game yang dimainkan. Minimax akan menghasilkan kemungkinan kekalahan yang sedikit dan memperbanyak kemungkinan kemenangan. Hasil yang didapatkan yaitu Minimax yang dikembangkan bersama Alpha Beta Pruning mampu membuat lawan berfikir layaknya manusia sehingga kecerdasan buatan didalamnya cocok untuk diterapkan. Hasil pengujian juga memberikan hasil 63% kemenangan bagi AI (Artificial Intelligence) yang digunakan, sehingga permainan menjadi menantang. Kata Kunci: Game, augmented reality, Minimax, papa

Planning For Non-Player Characters By Learning From Demonstration

In video games, state of the art non-player character (NPC) behavior generation typically depends on hard-coding NPC actions. In many game situations however, it is hard to foresee how an NPC should behave to appear intelligent or to accommodate human preferences for NPC behavior. We advocate the creation of a more flexible method to allow players (and developers) to train NPCs to execute novel behaviors which are not hard-coded. In particular, we investigate search-based planning approaches using demonstration to guide the search through high-dimensional spaces that represent the full state of the game. To this end, we developed the Training Graph heuristic, an extension of the Experience Graph heuristic, that guides a search smoothly and effectively even when a demonstration is unreachable in the search space, and ensures that more of the demonstrations are utilized to better train the NPC\u27s behavior. To deal with variance in the initial conditions of such planning problems, we have developed heuristics in the Multi-Heuristic A* framework to adapt demonstration trace data to new problems. We evaluate our approach in the Creation Engine game engine by modifying The Elder Scrolls V: Skyrim (Skyrim) to accommodate our NPC behavior generators and experiments. In Skyrim, players are given quests which are composed of several objectives. NPCs in the game sometimes accompany the player on quests, but state-of-the-art companion NPC AI is not sophisticated enough to behave according to arbitrary player desires. We hope that our work will lead to the creation of trainable NPC AI. This will enable novel gameplay mechanics for video game players and may augment video game production by allowing developers to train NPCs instead of hard-coding complex behaviors

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

Orchestrating Game Generation

The design process is often characterized by and realized through the iterative steps of evaluation and refinement. When the process is based on a single creative domain such as visual art or audio production, designers primarily take inspiration from work within their domain and refine it based on their own intuitions or feedback from an audience of experts from within the same domain. What happens, however, when the creative process involves more than one creative domain such as in a digital game? How should the different domains influence each other so that the final outcome achieves a harmonized and fruitful communication across domains? How can a computational process orchestrate the various computational creators of the corresponding domains so that the final game has the desired functional and aesthetic characteristics? To address these questions, this article identifies game facet orchestration as the central challenge for AI-based game generation, discusses its dimensions and reviews research in automated game generation that has aimed to tackle it. In particular, we identify the different creative facets of games, we propose how orchestration can be facilitated in a top-down or bottom-up fashion, we review indicative preliminary examples of orchestration, and we conclude by discussing the open questions and challenges ahead

Imitative learning for designing intelligent agents for video games

Over the past decades, video games have become increasingly popular and complex. Virtual worlds have gone a long way since the first arcades and so have the artificial intelligence (AI) techniques used to control agents in these growing environments. Tasks such as world exploration, constrained pathfinding or team tactics and coordination just to name a few are now default requirements for contemporary video games. However, despite its recent advances, video game AI still lacks the ability to learn. In this work, we attempt to break the barrier between video game AI and machine learning and propose a generic method allowing real-time strategy (RTS) agents to learn production strategies from a set of recorded games using supervised learning. We test this imitative learning approach on the popular RTS title StarCraft II and successfully teach a Terran agent facing a Protoss opponent new production strategies