"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

Making the water move: techno-historic limits in the game aesthetics of Myst and Doom.

This paper proposes that the technical limitations at the time of a game?s creation have an enormous impact on the overall aesthetic of any specific game, and also the traditions of the whole craft. Thus, an awareness of this aspect is critical to the useful analysis of games. However, this is often missing from current analyses of games. To illustrate both the significance of techno-historic limits, and several fundamental principals of digital technology, the landmark games Myst (Cyan, 1993) and Doom (id Software, 1993) are explored as examples of the evolution of game aesthetics over time. This leads to an examination of the future limits of the rendering of images and sounds, and how this may have an impact on future game aesthetics and genres

Improving Computer Game Bots\u27 behavior using Q-Learning

In modern computer video games, the quality of artificial characters plays a prominent role in the success of the game in the market. The aim of intelligent techniques, termed game AI, used in these games is to provide an interesting and challenging game play to a game player. Being highly sophisticated, these games present game developers with similar kind of requirements and challenges as faced by academic AI community. The game companies claim to use sophisticated game AI to model artificial characters such as computer game bots, intelligent realistic AI agents. However, these bots work via simple routines pre-programmed to suit the game map, game rules, game type, and other parameters unique to each game. Mostly, illusive intelligent behaviors are programmed using simple conditional statements and are hard-coded in the bots\u27 logic. Moreover, a game programmer has to spend considerable time configuring crisp inputs for these conditional statements. Therefore, we realize a need for machine learning techniques to dynamically improve bots\u27 behavior and save precious computer programmers\u27 man-hours. So, we selected Q-learning, a reinforcement learning technique, to evolve dynamic intelligent bots, as it is a simple, efficient, and online learning algorithm. Machine learning techniques such as reinforcement learning are know to be intractable if they use a detailed model of the world, and also requires tuning of various parameters to give satisfactory performance. Therefore, for this research we opt to examine Q-learning for evolving a few basic behaviors viz. learning to fight, and planting the bomb for computer game bots. Furthermore, we experimented on how bots would use knowledge learned from abstract models to evolve its behavior in more detailed model of the world. Bots evolved using these techniques would become more pragmatic, believable and capable of showing human-like behavior. This will provide more realistic feel to the game and provide game programmers with an efficient learning technique for programming these bots

Modelling Human-like Behavior through Reward-based Approach in a First-Person Shooter Game

We present two examples of how human-like behavior can be implemented in a model of computer player to improve its characteristics and decision-making patterns in video game. At first, we describe a reinforcement learning model, which helps to choose the best weapon depending on reward values obtained from shooting combat situations.Secondly, we consider an obstacle avoiding path planning adapted to the tactical visibility measure. We describe an implementation of a smoothing path model, which allows the use of penalties (negative rewards) for walking through \bad" tactical positions. We also study algorithms of path nding such as improved I-ARA* search algorithm for dynamic graph by copying human discrete decision-making model of reconsidering goals similar to Page-Rank algorithm. All the approaches demonstrate how human behavior can be modeled in applications with significant perception of intellectual agent actions

Modelling Human-like Behavior through Reward-based Approach in a First-Person Shooter Game

We present two examples of how human-like behavior can be implemented in a model of computer player to improve its characteristics and decision-making patterns in video game. At first, we describe a reinforcement learning model, which helps to choose the best weapon depending on reward values obtained from shooting combat situations.Secondly, we consider an obstacle avoiding path planning adapted to the tactical visibility measure. We describe an implementation of a smoothing path model, which allows the use of penalties (negative rewards) for walking through \bad" tactical positions. We also study algorithms of path nding such as improved I-ARA* search algorithm for dynamic graph by copying human discrete decision-making model of reconsidering goals similar to Page-Rank algorithm. All the approaches demonstrate how human behavior can be modeled in applications with significant perception of intellectual agent actions

Deep learning for video game playing

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

Human Behavior Models for Agents in Simulators and Games: Part II Gamebot Engineering with PMFserv

Many producers and consumers of legacy training simulator and game environments are beginning to envision a new era where psycho-socio-physiologic models could be interoperated to enhance their environments\u27 simulation of human agents. This paper explores whether we could embed our behavior modeling framework (described in the companion paper, Part 1) behind a legacy first person shooter 3D game environment to recreate portions of the Black Hawk Down scenario. Section 1 amplifies the interoperability needs and challenges confronting the field, presents the questions that are examined, and describes the test scenario. Sections 2 and 3 review the software and knowledge engineering methodology, respectively, needed to create the system and populate it with bots. Results (Section 4) and discussion (Section 5) reveal that we were able to generate plausible and adaptive recreations of Somalian crowds, militia, women acting as shields, suicide bombers, and more. Also, there are specific lessons learned about ways to advance the field so that such interoperabilities will become more affordable and widespread

Software techniques for improving head mounted displays to create comfortable user experiences in virtual reality

Head Mounted Displays (HMDs) allow users to experience Virtual Reality (VR) with a great level of immersion. Advancements in hardware technologies have led to a reduction in cost of producing good quality VR HMDs bringing them out from research labs to consumer markets. However, the current generation of HMDs suffer from a few fundamental problems that can deter their widespread adoption. For this thesis, we explored two techniques to overcome some of the challenges of experiencing VR when using HMDs. When experiencing VR with HMDs strapped to your head, even simple physical tasks like drinking a beverage can be difficult and awkward. We explored mixed reality renderings that selectively incorporate the physical world into the virtual world for interactions with physical objects. We conducted a user study comparing four rendering techniques that balance immersion in the virtual world with ease of interaction with the physical world. Users of VR systems often experience vection, the perception of self-motion in the absence of any physical movement. While vection helps to improve presence in VR, it often leads to a form of motion sickness called cybersickness. Prior work has discovered that changing vection (changing the perceived speed or moving direction) causes more severe cybersickness than steady vection (walking at a constant speed or in a constant direction). Based on this idea, we tried to reduce cybersickness caused by character movements in a First Person Shooter (FPS) game in VR. We propose Rotation Blurring (RB), uniformly blurring the screen during rotational movements to reduce cybersickness. We performed a user study to evaluate the impact of RB in reducing cybersickness and found that RB led to an overall reduction in sickness levels of the participants and delayed its onset. Participants who experienced acute levels of cybersickness benefited significantly from this technique