"It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games

Game agents such as opponents, non-player characters, and teammates are central to player experiences in many modern games. As the landscape of AI techniques used in the games industry evolves to adopt machine learning (ML) more widely, it is vital that the research community learn from the best practices cultivated within the industry over decades creating agents. However, although commercial game agent creation pipelines are more mature than those based on ML, opportunities for improvement still abound. As a foundation for shared progress identifying research opportunities between researchers and practitioners, we interviewed seventeen game agent creators from AAA studios, indie studios, and industrial research labs about the challenges they experienced with their professional workflows. Our study revealed several open challenges ranging from design to implementation and evaluation. We compare with literature from the research community that address the challenges identified and conclude by highlighting promising directions for future research supporting agent creation in the games industry.Comment: 7 pages, 3 figures, to be published in the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-20

DATA Agent

This paper introduces DATA Agent, a system which creates murder mystery adventures from open data. In the game, the player takes on the role of a detective tasked with finding the culprit of a murder. All characters, places, and items in DATA Agent games are generated using open data as source content. The paper discusses the general game design and user interface of DATA Agent, and provides details on the generative algorithms which transform linked data into different game objects. Findings from a user study with 30 participants playing through two games of DATA Agent show that the game is easy and fun to play, and that the mysteries it generates are straightforward to solve.peer-reviewe

Dynamic story writer for computer role-playing games

Applied project submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2019Computer Role-Playing Games (CRPG) are a genre of video games were the player controls at least one character, the character has attributes and relationships with other game objects and is important to the story. They are one of many entertainment media that have storytelling as one of its major components. The better the story component of a game the better the gameplay experience is. This project explores an approach to improving gameplay of RPG games by personalization of game stories. This is achieved by having an AI system generate the game story at run time by considering the decisions and choices the player makes. A benefit of this will be that all players will have unique stories and whenever a player restarts the game they will get a new story. This creates an amount of gameplay experience with each story and players get to be actively involved in the narrative direction of the game.Ashesi Universit

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

Player Behavior Modeling In Video Games

Player Behavior Modeling in Video Games In this research, we study players’ interactions in video games to understand player behavior. The first part of the research concerns predicting the winner of a game, which we apply to StarCraft and Destiny. We manage to build models for these games which have reasonable to high accuracy. We also investigate which features of a game comprise strong predictors, which are economic features and micro commands for StarCraft, and key shooter performance metrics for Destiny, though features differ between different match types. The second part of the research concerns distinguishing playing styles of players of StarCraft and Destiny. We find that we can indeed recognize different styles of playing in these games, related to different match types. We relate these different playing styles to chance of winning, but find that there are no significant differences between the effects of different playing styles on winning. However, they do have an effect on the length of matches. In Destiny, we also investigate what player types are distinguished when we use Archetype Analysis on playing style features related to change in performance, and find that the archetypes correspond to different ways of learning. In the final part of the research, we investigate to what extent playing styles are related to different demographics, in particular to national cultures. We investigate this for four popular Massively multiplayer online games, namely Battlefield 4, Counter-Strike, Dota 2, and Destiny. We found that playing styles have relationship with nationality and cultural dimensions, and that there are clear similarities between the playing styles of similar cultures. In particular, the Hofstede dimension Individualism explained most of the variance in playing styles between national cultures for the games that we examined

Hexgen Roguevania

This project is inspired by procedural generation methods, Metroid, Castlevania, Rogue, and modern roguelikes. This project is structured around the notion of how procedural game content can be experienced when it is tied directly to player progression. The focus of this project incorporates procedurally generated content so that it can be altered during gameplay. In a sense, this idea brings level content to the player, instead of the player travelling to new zones. The resulting game world starts visually coherent, changing into a world that resembles a patchwork of different biomes crafted by the player’s decisions to progress the game’s difficulty

Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birds

This paper investigates the suitability of using Generative Adversarial Networks (GANs) to generate stable structures for the physics-based puzzle game Angry Birds. While previous applications of GANs for level generation have been mostly limited to tile-based representations, this paper explores their suitability for creating stable structures made from multiple smaller blocks. This includes a detailed encoding/decoding process for converting between Angry Birds level descriptions and a suitable grid-based representation, as well as utilizing state-of-the-art GAN architectures and training methods to produce new structure designs. Our results show that GANs can be successfully applied to generate a varied range of complex and stable Angry Birds structures.Comment: 11 pages, 10 figures, 2 tables, Accepted at the 19th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 23