Generating Levels That Teach Mechanics

The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.Comment: 8 pages, 7 figures, PCG Workshop at FDG 2018, 9th International Workshop on Procedural Content Generation (PCG2018

Poker as a Domain of Expertise

Poker is a game of skill and chance involving economic decision-making under uncertainty. It is also a complex but well-defined real-world environment with a clear rule-structure. As such, poker has strong potential as a model system for studying high-stakes, high-risk expert performance. Poker has been increasingly used as a tool to study decision-making and learning, as well as emotion self-regulation. In this review, we discuss how these studies have begun to inform us about the interaction between emotions and technical skill, and how expertise develops and depends on these two factors. Expertise in poker critically requires both mastery of the technical aspects of the game, and proficiency in emotion regulation; poker thus offers a good environment for studying these skills in controlled experimental settings of high external validity.We conclude by suggesting ideas for future research on expertise, with new insights provided by poker.Peer reviewe

The role of emotions mediating decision-making among successful poker players

Thesis (Ed.D.)--Boston UniversityThis study was designed to explore the role of emotions mediating the decision-making process in poker. The research questions included: (1) What emotions are reported by poker players? (2) What are the poker-related predictors of the reported emotions? (3) How do the reported emotions influence decision-making? A qualitative approach was chosen for the eight intermediate poker players in the study. Data analyzed according to content analysis derived from semi-structured retrospective interviews and a think aloud protocol offered evidence that emotions impacted the decision-making process of intermediate poker players. Six distinct emotions were found to influence the decision-making process in poker: Pride, excitement, happiness, anxiety, frustration, and anger. Findings confirmed prior research of the role of frustration and anger in influencing suboptimal decision-making. Three main findings deviated from previous research: Positive emotions of pride and excitement were found to impact suboptimal decision-making, the negative emotion of anxiety was found to impact optimal decision-making, and the role of experience facilitated a healthier interpretation of, and reaction to, multiple emotions. The findings in this study provide practical and academic applications for researchers, poker players, and poker consultants. A number of different directions for future research are suggested, including more observation of naturalistic poker play, using real money for naturalistic poker play, and comparing the emotional experience of different stratum of experts and intermediates. Poker players could improve profit margins through using more active, facilitative forms of coping for strong emotional reactions, while poker consultants could help clients in proactively entering emotional zones that facilitate optimal decision-making.2024-09-3

Famtile: An Algorithm For Learning High-level Tactical Behavior From Observation

This research focuses on the learning of a class of behaviors defined as high-level behaviors. High-level behaviors are defined here as behaviors that can be executed using a sequence of identifiable behaviors. Represented by low-level contexts, these behaviors are known a priori to learning and can be modeled separately by a knowledge engineer. The learning task, which is achieved by observing an expert within simulation, then becomes the identification and representation of the low-level context sequence executed by the expert. To learn this sequence, this research proposes FAMTILE - the Fuzzy ARTMAP / Template-Based Interpretation Learning Engine. This algorithm attempts to achieve this learning task by constructing rules that govern the low-level context transitions made by the expert. By combining these rules with models for these low-level context behaviors, it is hypothesized that an intelligent model for the expert can be created that can adequately model his behavior. To evaluate FAMTILE, four testing scenarios were developed that attempt to achieve three distinct evaluation goals: assessing the learning capabilities of Fuzzy ARTMAP, evaluating the ability of FAMTILE to correctly predict expert actions and context choices given an observation, and creating a model of the expert\u27s behavior that can perform the high-level task at a comparable level of proficiency

AtDelfi: Automatically Designing Legible, Full Instructions For Games

This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (AuTomatically DEsigning Legible, Full Instructions for games) was created to investigate procedural generation of instructions that teach players how to play video games. We present a representation of game rules and mechanics using a graph system as well as a tutorial generation method that uses said graph representation. We demonstrate the concept by testing it on games within the General Video Game Artificial Intelligence (GVG-AI) framework; the paper discusses tutorials generated for eight different games. Our findings suggest that a graph representation scheme works well for simple arcade style games such as Space Invaders and Pacman, but it appears that tutorials for more complex games might require higher-level understanding of the game than just single mechanics.Comment: 10 pages, 11 figures, published at Foundations of Digital Games Conference 201