This paper explores adaptive problem solving with a game designed to support the development of problem-solving skills. Using an adaptive, AI-powered puzzle game, our adaptive problem-solving system dynamically generates pathfinding-based puzzles using a genetic algorithm, tailoring the difficulty of each puzzle to individual players in an online real-time approach. A player-modeling system records user interactions and informs the generation of puzzles to approximate a target difficulty level based on various metrics of the player. By combining procedural content generation with online adaptive difficulty adjustment, the system aims to maintain engagement, mitigate frustration, and maintain an optimal level of challenge. A pilot user study investigates the effectiveness of this approach, comparing different types of adaptive difficulty systems and interpreting players' responses. This work lays the foundation for further research into emotionally informed player models, advanced AI techniques for adaptivity, and broader applications beyond gaming in educational settings.

Modern adaptive games require nuanced player understanding, yet most models use simplified 5-10 category taxonomies that fail to capture diversity. Behavioral clustering cannot distinguish players with different motivations who act similarly. We present a systematic evaluation of multi-modal classification at scale, combining behavioral telemetry with semantic context to support 36 player profiles. Using 19,413 gameplay sessions from an AI-controlled text-based RPG, we compared behavioral-only baselines with multi-modal approaches that integrate action sequences and semantic descriptions. Traditional clustering achieved only 10% accuracy for 36-category classification, limited by semantic conflation where opposite actions produced identical features. Our multi-modal LSTM processing action-text pairs improved accuracy to 21%, showing both potential and limits of non-conversational data. Analysis by behavioral complexity revealed that non-neutral profiles reached 42% accuracy (15x above random), while neutral profiles dropped to 25% (9x above random). Identical actions such as "help the merchant" cannot reveal whether a player is neutral or strategically waiting. Without access to reasoning, even multi-modal models struggle, though above-baseline results confirm a meaningful signal. Since prediction beyond 20 categories remains unexplored, our findings establish benchmarks for complex player modeling. Behavioral data alone plateaus near 10% for 36 categories, while multi-modal integration enables 25%. For designers, this shows that personality-based adaptation requires conversational interaction, as predefined choices cannot capture intent. Our evaluation at 36-category scale offers guidance for building adaptive games that better understand their players.

Contemporary artificial intelligence (AI) development largely centers on rational decision-making, valued for its measurability and suitability for objective evaluation. Y et in real-world contexts, an intelligent agent's decisions are shaped not only by logic but also by deeper influences such as beliefs, values, and preferences. The diversity of human decision-making styles emerges from these differences, highlighting that "style" is an essential but often overlooked dimension of intelligence. This dissertation introduces playstyle as an alternative lens for observing and analyzing the decision-making behavior of intelligent agents, and examines its foundational meaning and historical context from a philosophical perspective. By analyzing how beliefs and values drive intentions and actions, we construct a two-tier framework for style formation: the external interaction loop with the environment and the internal cognitive loop of deliberation. On this basis, we formalize style-related characteristics and propose measurable indicators such as style capacity, style popularity, and evolutionary dynamics. The study focuses on three core research directions: (1) Defining and measuring playstyle, proposing a general playstyle metric based on discretized state spaces, and extending it to quantify strategic diversity and competitive balance; (2) Expressing and generating playstyle, exploring how reinforcement learning and imitation learning can be used to train agents exhibiting specific stylistic tendencies, and introducing a novel approach for human-like style learning and modeling; and (3) Practical applications, analyzing the potential of these techniques in domains such as game design and interactive entertainment. Finally, the dissertation outlines future extensions, including the role of style as a core element in building artificial general intelligence (AGI). By investigating stylistic variation, we aim to rethink autonomy, value expression, and even offer a tangible perspective on the ultimate i philosophical question: What is the soul?

Recent years have seen a growing interest in player modeling, which supports the creation of player-adaptive digital games. A central problem of player modeling is goal recognition, which aims to recognize players' intentions from observable gameplay behaviors. Player goal recognition offers the promise of enabling games to dynamically adjust challenge levels, perform procedural content generation, and create believable NPC interactions. A growing body of work is investigating a wide range of machine learning-based goal recognition models. In this paper, we introduce GOALIE, a multidimensional framework for evaluating player goal recognition models.

A significant amount of research has been devoted to automatic personalization in digital applications, especially in Internet applications Computer games represent an ideal research domain for the next [8]. As the content of the Internet services grows, personalized generation of personalized digital applications. This paper presents applications such as recommendation systems help to mitigate information a player-centered framework of AI for game personalization, complementary overload and decision fatigue [8]. This body of work to the commonly used system-centered approaches.

This paper focuses on building personalized player models solely from player behavior in the context of adaptive games. We present two main contributions: The first is a novel approach to player modeling based on multi-armed bandits (MABs). This approach addresses, at the same time and in a principled way, both the problem of collecting data to model the characteristics of interest for the current player and the problem of adapting the interactive experience based on this model. Second, we present an approach to evaluating and fine-tuning these algorithms prior to generating data in a user study. This is an important problem, because conducting user studies is an expensive and labor-intensive process; therefore, an ability to evaluate the algorithms beforehand can save a significant amount of resources. We evaluate our approach in the context of modeling players' social comparison orientation (SCO) and present empirical results from both simulations and real players.

This paper focuses on "tracing player knowledge" in educational games. Specifically, given a set of concepts or skills required to master a game, the goal is to estimate the likelihood with which the current player has mastery of each of those concepts or skills. The main contribution of the paper is an approach that integrates machine learning and domain knowledge rules to find when the player applied a certain skill and either succeeded or failed. This is then given as input to a standard knowledge tracing module (such as those from Intelligent Tutoring Systems) to perform knowledge tracing. We evaluate our approach in the context of an educational game called "Parallel" to teach parallel and concurrent programming with data collected from real users, showing our approach can predict students skills with a low mean-squared error.

Experience Management studies AI systems that automatically adapt interactive experiences such as games to tailor to specific players and to fulfill design goals. Although it has been explored for several decades, existing work in experience management has mostly focused on single-player experiences. This paper is a first attempt at identifying the main challenges to expand EM to multi-player/multi-user games or experiences. We also make connections to related areas where solutions for similar problems have been proposed (especially group recommender systems) and discusses the potential impact and applications of multi-player EM.

In this paper we discuss some of the ethical challenges that may arise from player modeling. Player modeling is used in modern games e.g. to enable various kinds of game play, to optimize games for specific players, and to maximize the monetization of games. In this paper, we propose that apply- ing player modeling implies serious ethical questions, since it impacts how players spend their leisure time and money, affects their social relations, and changes computer games as ethical artifacts. We source categories of ethical issues in the application of artificial intelligence (AI) from work on AI ethics and using these we provide several specific examples of ethical issues in player modeling. Building from the exam- ples, we suggest establishing a framework for understanding ethical issues in player modeling and we propose a number of methodological approaches to address the identified challenges.

A growing research community is working towards procedurally generating content for computer games and simulation applications with various player modeling techniques. In this paper, we present a two-step procedural content generation framework to minimize players' frustration and/or boredom according to player feedback and gameplay features. In the first step, we dynamically categorize the player styles based on a simple questionnaire beforehand and the gameplay features. In the second step, two player models (frustration and boredom) are built for each player style category. A ranking algorithm is utilized for player modeling to address two problems inherent in player feedback: inconsistency and inaccuracy. Experiment results on a testbed game show that our framework can generate less boring/frustrating levels with very high probabilities.