Non-player Characters 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 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 a mathematically simple approach significantly improves over common strategies typically found in games, and can achieve results similar to much more expensive look-up tree approaches. This work has applications in practical game design, showing that simple, computationally efficient target selection can make an excellent target selection heuristic.
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.
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.
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.
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.