"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

Analysis of Matchmaking Optimization Systems Potential in Mobile eSports

Matchmaking systems are one of the core features of experience in online gaming. They influence player satisfaction, engagement, and churn risk. The paper looks into the current state of the theoretical and practical implementation of such systems in the mobile gaming industry. We propose a basic classification of matchmaking systems into random and quasi-random, skill-based, role-based, technical factor-based, and engagement based. We also offer an analysis of matchmaking systems in 16 leading mobile Esport games. The dominant industry solution is skill and rank based systems with a different level of skill depth measurement. In the further part of the paper, we present a theoretical model of engagement and a time-optimized model

GAMESPECT: A Composition Framework and Meta-Level Domain Specific Aspect Language for Unreal Engine 4

Game engine programming involves a great number of software components, many of which perform similar tasks; for example, memory allocation must take place in the renderer as well as in the creation routines while other tasks such as error logging must take place everywhere. One area of all games which is critical to the success of the game is that of game balance and tuning. These balancing initiatives cut across all areas of code from the player and AI to the mission manager. In computer science, we’ve come to call these types of concerns “cross cutting”. Aspect oriented programming was developed, in part, to solve the problems of cross cutting: employing “advice” which can be incorporated across different pieces of functionality. Yet, despite the prevalence of a solution, very little work has been done to bring cross cutting to game engine programming. Additionally, the discipline involves a heavy amount of code rewriting and reuse while simultaneously relying on many common design patterns that are copied from one project to another. In the case of game balance, the code may be wildly different across two different games despite the fact that similar tasks are being done. These two problems are exacerbated by the fact that almost every game engine has its own custom DSL (domain specific language) unique to that situation. If a DSL could showcase the areas of cross cutting concerns while highlighting the ability to capture design patterns that can be used across games, significant productivity savings could be achieved while simultaneously creating a common thread for discussion of shared problems within the domain. This dissertation sought to do exactly that- create a metalanguage called GAMESPECT which supports multiple styles of DSLs while bringing aspect-oriented programming into the DSL’s to make them DSAL (domain specific aspect languages). The example cross cutting concern was game balance and tuning since it’s so pervasive and important to gaming. We have created GAMESPECT as a language and a composition framework which can assist engine developers and game designers in balancing their games, forming one central place for game balancing concerns even while these concerns may cross different languages and locations inside the source code. Generality was measured by showcasing the composition specifications in multiple contexts and languages. In addition to evaluating generality and performance metrics, effectiveness was be measured. Specifically, comparisons were made between a balancing initiative when performed with GAMESPECT vs a traditional methodology. In doing so, this work shows a clear advantage to using a Metalanguage such as GAMESPECT for this task. In general, a line of code reduction of 9-40% per task was achieved with negligible effects to performance. The use of a metalanguage in Unreal Engine 4 is a starting point to further discussions concerning other game engines. In addition, this work has implications beyond video game programming. The work described highlights benefits which might be achieved in other disciplines where design pattern implementations and cross-cutting concern usage is high; the real time simulation field and the field of Windows GUI programming are two examples of future domains

Effective reinforcement learning for collaborative multi-agent domains

Ph.DDOCTOR OF PHILOSOPH

Dynamically adjusting game-play in 2D platformers using procedural level generation

The rapid growth of the entertainment industry has presented the requirement for more efficient development of computerized games. Importantly, the diversity of audiences that participate in playing games has called for the development of new technologies that allow games to address users with differing levels of skills and preferences. This research presents a systematic study that explored the concept of dynamic difficulty using procedural level generation with interactive evolutionary computation. Additionally, the design, development and trial of computerized agents the play game levels in the place of a human player is detailed. The work presented in this thesis provides a solution to the rapid growth of the entertainment industry whilst providing a more effective means for developing computerized games

Deep Reinforcement Learning using Capsules in Advanced Game Environments

Reinforcement Learning (RL) is a research area that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games. This success is primarily due to vast capabilities of Convolutional Neural Networks (ConvNet), enabling algorithms to extract useful information from noisy environments. Capsule Network (CapsNet) is a recent introduction to the Deep Learning algorithm group and has only barely begun to be explored. The network is an architecture for image classification, with superior performance for classification of the MNIST dataset. CapsNets have not been explored beyond image classification. This thesis introduces the use of CapsNet for Q-Learning based game algorithms. To successfully apply CapsNet in advanced game play, three main contributions follow. First, the introduction of four new game environments as frameworks for RL research with increasing complexity, namely Flash RL, Deep Line Wars, Deep RTS, and Deep Maze. These environments fill the gap between relatively simple and more complex game environments available for RL research and are in the thesis used to test and explore the CapsNet behavior. Second, the thesis introduces a generative modeling approach to produce artificial training data for use in Deep Learning models including CapsNets. We empirically show that conditional generative modeling can successfully generate game data of sufficient quality to train a Deep Q-Network well. Third, we show that CapsNet is a reliable architecture for Deep Q-Learning based algorithms for game AI. A capsule is a group of neurons that determine the presence of objects in the data and is in the literature shown to increase the robustness of training and predictions while lowering the amount training data needed. It should, therefore, be ideally suited for game plays.Comment: Master Thesis in Computer Scienc

A usability assessment for a career planning educational video game

This study focused on the design, implementation and usability assessment of an educational 2D iPad job matching game The Place You’ll Go (TPYG), which meant for matching student skill sets with career profiles. The development of the game is conducted in collaboration with Purdue University’s Krannert School of Management and Polytech Institute. A total of 7 subjects, as high school teachers, participated in the usability study. TPYG as one possible solution for job matching data visualization, did not provide players with a good experience. However, conclusions and findings can be used in similar education game development. Based on survey and analysis, new feasible and scientific plans were made for future development