Adaptive virtual reality horror games based on Machine learning and player modeling

This paper develops a new approach for estimating an interpretable, relational model of a black-box autonomous agent that can plan and act. Our main contributions are a new paradigm for estimating such models using a minimal query interface with the agent, and a hierarchical querying algorithm that generates an interrogation policy for estimating the agent's internal model in a vocabulary provided by the user. Empirical evaluation of our approach shows that despite the intractable search space of possible agent models, our approach allows correct and scalable estimation of interpretable agent models for a wide class of black-box autonomous agents. Our results also show that this approach can use predicate classifiers to learn interpretable models of planning agents that represent states as images.

Asking the Right Questions: Learning Interpretable Action Models Through Query Answering

Towards a Conceptual Framework of Comprehensive Video Game Player Profiles: Player Models, Mental Models, and Behavior Models

When rank and skill do not coincide in competitive games, this might be a sign of issues such as boosting, smurfing, and trolling occur. The fair gaming culture of online gaming is disrupted and offended by cheating like boosting, smurfing, and trolling. The player's play style must be used to determine the rank that appears in account information. In this study, we classified League of Legends' low and high tiers using a sequence-based CNN-LSTM model. Using input perturbation, the model can explain its own importance for certain features. The experimental progress: First, we selected features that show a difference between tiers by an extracted score estimating a cumulative sum graph. Second, we construct the dataset format variously with variable or fixed sequence length, compare performance, and analyze the pros and cons. Finally, we consider the possibility of early detection by measuring performance over game elapsed time. Along with the experiment, a rank classification performance of the model achieved AUC 0.9036 and found that we can distinguish from the 24 minutes after the start of the game. In addition, We derived that ccReduction and MinionsKilied were the information that had the most influence on skills among various features.

Justice League: Time-series Game Player Pattern Detection to Discover Rank-Skill Mismatch

When rank and skill do not coincide in competitive games, this might be a sign of issues such as boosting, smurfing, and trolling occur. The fair gaming culture of online gaming is disrupted and offended by cheating like boosting, smurfing, and trolling. The player's play style must be used to determine the rank that appears in account information. In this study, we classified League of Legends' low and high tiers using a sequence-based CNN-LSTM model. Using input perturbation, the model can explain its own importance for certain features. The experimental progress: First, we selected features that show a difference between tiers by an extracted score estimating a cumulative sum graph. Second, we construct the dataset format variously with variable or fixed sequence length, compare performance, and analyze the pros and cons. Finally, we consider the possibility of early detection by measuring performance over game elapsed time. Along with the experiment, a rank classification performance of the model achieved AUC 0.9036 and found that we can distinguish from the 24 minutes after the start of the game. In addition, We derived that ccReduction and MinionsKilied were the information that had the most influence on skills among various features. 

Player modeling attempts to create a computational model which accurately approximates a player’s behavior in a game. Most player modeling techniques rely on domain knowledge and are not transferable across games. Additionally, player models do not currently yield any explanatory insight about a player’s cognitive processes, such as the creation and refinement of mental models. In this paper, we present our findings with using action model learning (AML), in which an action model is learned given data in the form of a play trace, to learn a player model in a domain-agnostic manner. We demonstrate the utility of this model by introducing a technique to quantitatively estimate how well a player understands the mechanics of a game. We evaluate an existing AML algorithm (FAMA) for player modeling and develop a novel algorithm called Blackout that is inspired by player cognition. We compare Blackout with FAMA using the puzzle game Sokoban and show that Blackout generates better player models.

pdf/towards-action-model-learning-for-player-modeling-1g0frv09tg.pdf

Towards Action Model Learning for Player Modeling

Player modeling attempts to create a computational model which accurately approximates a player's behavior in a game. Most player modeling techniques rely on domain knowledge and are not transferable across games. Additionally, player models do not currently yield any explanatory insight about a player's cognitive processes, such as the creation and refinement of mental models. In this paper, we present our findings with using action model learning (AML), in which an action model is learned given data in the form of a play trace, to learn a player model in a domain-agnostic manner. We demonstrate the utility of this model by introducing a technique to quantitatively estimate how well a player understands the mechanics of a game. We evaluate an existing AML algorithm (FAMA) for player modeling and develop a novel algorithm called Blackout that is inspired by player cognition. We compare Blackout with FAMA using the puzzle game Sokoban and show that Blackout generates better player models.

Expert-crafted puzzle games are notable for their ability to wordlessly teach players deep and interesting consequences of mechanics. Cognitive science theories suggest that this learning happens through an iterative process of hypothesizing, failure, and revision of mental models. In this work, we pose the question of what it would take to generate puzzle game levels and progressions that best support player satisfaction and systematic understanding of mechanics by integrating an understanding of player mental models. We present a preliminary investigation consisting of a new puzzle game Laserverse, whose mechanics are designed to interact with each other in a wide array of combinations, a set of handauthored levels that combine these mechanics hierarchically, and an algorithm for organizing the levels into progressions to test hypotheses based on a theory of mental model formation. This work sets the stage for empirical validation of puzzle game design principles, which in turn inform a new approach to player modeling based on mental model matching theory.

/pdf/generating-puzzle-progressions-to-study-mental-model-1kgu1z893f.pdf

Generating Puzzle Progressions to Study Mental Model Matching.

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Towards Action Model Learning for Player Modeling

Towards Action Model Learning for Player Modeling

Generating Puzzle Progressions to Study Mental Model Matching.