Player traces can result in a vast amount of high-dimensional data. This information is crucial to game designers trying to understand player behaviour and for tailoring the game experience. In this work we investigate different approaches to clustering player traces in order to expose useful game information. We consider three trace similarity metrics, as well as two clustering algorithms. We evaluate and compare the different approaches using trivial and non-trivial game levels from different game genres, giving guidance on clustering and metric choices, and illustrating the potential value of cluster information.
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Enemy observers, such as cameras and guards, are common elements that provide challenge to many stealth and combat games. Defining the exact placement and movement of such entities, however, is a non-trivial process, requiring a designer balance level-difficulty, coverage, and representation of realistic behaviours. In this work we explore systems for procedurally generating both camera and guard placement in a stealth game context. For the former we use an approach based on weakening theoretical results for optimal camera placement, while for the latter we perform automatic roadmap construction, generating more specific patrol behaviours through a grammar-based technique. We evaluate both approaches with a non-trivial implementation in Unity3D, and apply quantitative metrics to demonstrate how different parametrizations can be used to control level difficulty without sacrificing believability.
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Combat and stealth games give players the option of engaging or avoiding enemy agents at different points. Level- design in this context is complex, however, requiring a designer to understand how different design choices impact difficulty under multiple play-styles. In this work we describe a unified algorithmic approach that can perform abstract analysis of both combat and stealth behaviours. Our proposed solution builds on an existing stealth-level analysis tool, incorporating combat activities into the abstraction in order to allow exploration of level feasibility and difficulty. We demonstrate our approach on a non-trivial example level, showing how such a tool can be used to evaluate and control the player experience.
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Level design for stealth games requires the ability to explore and understand the possible paths players may take through a given scenario and how they are impacted by different design choices. Good tool support can help by demonstrating the existence of such paths, but for rapid, interactive design, the relative difficulty of possible solutions also needs to be quantified, in a way that correlates well with human perception of risk. Here we propose and evaluate three different metrics for defining and quantifying the risk of stealthy paths. We validate and compare these measures through a small human study, showing that a simple path-distance measure correlates best with human judgement. An evaluation of a non-trivial stealth scenario demonstrates the practicality of our approach, and shows how such measures can be useful in understanding a level design.
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Non-player Characters (NPCs) that accompany the player enable a single player to participate in team-based experiences, improving immersion and allowing for more complex gameplay. In this context, an Artificial Intelligence (AI) teammate should make good combat decisions, supporting the player and optimizing combat resolution. Here we investigate the target selection problem, which consists of picking the optimal enemy as a target in a modern war game. We look at how the companion's different strategies can influence the outcome of combat, and by analyzing a variety of non-trivial First Person Shooter (FPS) scenarios show that an intuitively simple approach has good mathematical justification, improves over other common strategies typically found in games, and can even achieve results similar to much more expensive look-up tree approaches. This work has applications in practical game design, verifying that simple, computationally efficient target selection can make an excellent target selection heuristic.
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Non-player characters (NPCs) in video games tend to be easily recognized by human players, reducing the sense of immersion and limiting the complexity of character interactions. In this paper we study various aspects of the NPCs performance and how it differs from human players. We provide catego- rization and metrics for quantifying some aspects of the NPCs performance and provide an in-depth analysis of the behavior of NPCs. We detail how movements, interactions, use of items, and relying on static decision-making schemes result in markedly different behaviors from humans in the popular FPS Quake III. In addition, we propose a framework relying on a special kind of influence map, a pheromone map, which can lead to a more adaptive human-like behavior. These maps can efficiently give a summary of the events in the game world, be adaptive in nature, and be effectively used in the decision making process of NPCs.
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Non-player characters that act as companions for players are a common feature of modern games. Designing a companion that reacts appropriately to the player's experience, however, is not a trivial task, and even current, triple-A titles tend to provide companions that are either static in behaviour or evince only superficial connection to player activity. To address this issue we develop an adaptive companion that analyses the player's in-game experience and behaves accordingly. We evaluate our adaptive companion in different, non-trivial scenarios, as well as compare our proposed model to a straightforward approach to adaptivity based on Dynamic Difficulty Adjustment (DDA). The data collected demonstrates that the adaptive companion has more influence over the player's experience and that there exists an orthogonality between our companion adaptivity and the more traditional combat/health scaling approaches to difficulty adjustment. Using adaptive companions is a step forward in offering meaningful and engaging games to players.
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This paper investigates adaptive games mechanics and how to implement them. First, a comprehensive review of existing adaptive models is presented. Next, we propose a new adaptive model, which combines dynamic difficulty adaptation, the player’s performance, and adaptive flow. An implementation of these new adaptive mechanics is presented in the form of a simple serious game called Number to Number Combat. This game was released freely on the internet in order to be tested by the gaming community. It has shown very promising results that will help us to improve our adaptive model.
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Platformer games let players solve real-time, physics-based puzzles by jumping and moving around to reach different goals. Designing levels for this context is a non-trivial task; the placement of well-timed jumps, moving platforms, in-teresting traps, etc., has a complex relationship to in-game challenge and the existence of possible solutions. In this work, we describe three different search algorithms (A* , MCTS and RRT) that could be used to simulate player be-haviour in the platformer domain. We evaluate and compare the three approaches applied to three non-trivial levels, show-ing a possible iterative workflow of use to designers, and re-search progress in designing search algorithms for platformer games
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Stealth game mechanics rely on a suitably difficult distribution of enemy observers, the placement of which is typically a manual process. Here we investigate an automatic process for placement of observer opponents. We use a Monte-Carlo approach to generate randomized enemy positions and motions and combine this with a stealth path-planning and analysis framework. This allows us to ensure feasibility of the level design, and also measure relative difficulty. Initial results using this process compare placement of both mobile and static guards (rotating cameras), and let us explore the impact on level difficulty produced by different kinds of enemy observer agents.
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Stealthy movement is an important part of many games in the First Person Shooter (FPS) and Role Playing Games (RPG) genres. Structuring a game level to match stealth goals, however, is difficult, and can depend on subtle and fragile interactions between the game space, enemy motion, and other factors. Here we apply a probabilistic path-finding approach to efficiently analyze a 2D space and find stealthy paths. This approach naturally accommodates variation in the level design, numbers and movements of enemies, fields of view, and player start and goal placement. Our design is integrated directly into the Unity 3D game development framework, allowing for interactive and highly dynamic exploration of how different virtual spaces and enemy configurations affect the potential for stealthly movement by players, or other NPCs.
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This chapter begins with an introduction to different concepts evolving around the adaptive difficulty in video games (i.e. problematic definition, existing models of dynamic difficulty adjustment, evaluating the player’s experience, transposing the player’s skills into numerical values, using these numerical values as seeds for the difficulty level, etc.). Further on, this chapter covers the implementation of a novel adaptive model and the validation of such a model. This model uses a normal distribution system (ELO ranking) to determine the player’s skill level and then adapt the difficulty to their needs. In order to validate this model, 42 players play-tested two versions of the game, one with adaptive difficulty and one without any difficulty adaptation.
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Most video games suffer from system inflexibility, which is responsible for the player to give up on the game. As the players are expecting fun produced by different experiences and gaming sessions, the game should be able to adapt itself to the player and meet their expectations. It is important for the player to experience fun as it is what the game industry relies on to keep the player consume their products. Fun is not trivial to define and create, in order to understand fun in game, researchers have been using flow theory that provides a strong understanding of an emotion state that is linked to fun.
It is undeniable that every game should provide the possibility for the player to experience flow, which means it has to provide an understanding of the player' skills so it can adapt the game proposed challenge to their specific abilities. The goal of this research is to address this issue by proposing a new adaptive model for dynamically adjusting, in real-time, the difficulty level of a game in order to enhance the player's experience. This model has been implemented for validation in the form of a simple calculation/combat serious game called Number to Number. An experiment has been conducted with this prototype where 150 playing sessions have been completed by 32 players. Each player had to answer a detailed questionnaire on their playing experience. The results of this experiment were very promising, showing the value of the proposed approach and giving us clues for improving the model.
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