Statistical Modelling of Level Difficulty in Puzzle Games
- URL: http://arxiv.org/abs/2107.03305v2
- Date: Thu, 8 Jul 2021 08:21:25 GMT
- Title: Statistical Modelling of Level Difficulty in Puzzle Games
- Authors: Jeppe Theiss Kristensen, Arturo Valdivia, Paolo Burelli
- Abstract summary: We formalise a model of level difficulty for puzzle games that goes beyond the classical probability of success.
The model is fitted and evaluated on a dataset collected from the game Lily's Garden by Tactile Games.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Successful and accurate modelling of level difficulty is a fundamental
component of the operationalisation of player experience as difficulty is one
of the most important and commonly used signals for content design and
adaptation. In games that feature intermediate milestones, such as completable
areas or levels, difficulty is often defined by the probability of completion
or completion rate; however, this operationalisation is limited in that it does
not describe the behaviour of the player within the area.
In this research work, we formalise a model of level difficulty for puzzle
games that goes beyond the classical probability of success. We accomplish this
by describing the distribution of actions performed within a game level using a
parametric statistical model thus creating a richer descriptor of difficulty.
The model is fitted and evaluated on a dataset collected from the game Lily's
Garden by Tactile Games, and the results of the evaluation show that the it is
able to describe and explain difficulty in a vast majority of the levels.
Related papers
- Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization [126.27645170941268]
We present Easy2Hard-Bench, a collection of 6 benchmark datasets spanning various domains.
Each problem within these datasets is annotated with numerical difficulty scores.
We provide a comprehensive analysis of their performance and generalization capabilities across varying levels of difficulty.
arXiv Detail & Related papers (2024-09-27T03:49:56Z) - Towards Explainable and Interpretable Musical Difficulty Estimation: A Parameter-efficient Approach [49.2787113554916]
Estimating music piece difficulty is important for organizing educational music collections.
Our work employs explainable descriptors for difficulty estimation in symbolic music representations.
Our approach, evaluated in piano repertoire categorized in 9 classes, achieved 41.4% accuracy independently, with a mean squared error (MSE) of 1.7.
arXiv Detail & Related papers (2024-08-01T11:23:42Z) - Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on
Different Methods to Combine Player Analytics and Simulated Data [0.0]
A common practice consists of creating metrics out of data collected by player interactions with the content.
This allows for estimation only after the content is released and does not consider the characteristics of potential future players.
In this article, we present a number of potential solutions for the estimation of difficulty under such conditions.
arXiv Detail & Related papers (2024-01-30T20:51:42Z) - The Unreasonable Effectiveness of Easy Training Data for Hard Tasks [84.30018805150607]
We present the surprising conclusion that current pretrained language models often generalize relatively well from easy to hard data.
We demonstrate this kind of easy-to-hard generalization using simple finetuning methods like in-context learning, linear heads, and QLoRA.
We conclude that easy-to-hard generalization in LMs is surprisingly strong for the tasks studied.
arXiv Detail & Related papers (2024-01-12T18:36:29Z) - Ordinal Regression for Difficulty Estimation of StepMania Levels [18.944506234623862]
We formalize and analyze the difficulty prediction task on StepMania levels as an ordinal regression (OR) task.
We evaluate many competitive OR and non-OR models, demonstrating that neural network-based models significantly outperform the state of the art.
We conclude with a user experiment showing our trained models' superiority over human labeling.
arXiv Detail & Related papers (2023-01-23T15:30:01Z) - Personalized Game Difficulty Prediction Using Factorization Machines [0.9558392439655011]
We contribute a new approach for personalized difficulty estimation of game levels, borrowing methods from content recommendation.
We are able to predict difficulty as the number of attempts a player requires to pass future game levels, based on observed attempt counts from earlier levels and levels played by others.
Our results suggest that FMs are a promising tool enabling game designers to both optimize player experience and learn more about their players and the game.
arXiv Detail & Related papers (2022-09-06T08:03:46Z) - Towards Objective Metrics for Procedurally Generated Video Game Levels [2.320417845168326]
We introduce two simulation-based evaluation metrics to measure the diversity and difficulty of generated levels.
We demonstrate that our diversity metric is more robust to changes in level size and representation than current methods.
The difficulty metric shows promise, as it correlates with existing estimates of difficulty in one of the tested domains, but it does face some challenges in the other domain.
arXiv Detail & Related papers (2022-01-25T14:13:50Z) - Generating Diverse and Competitive Play-Styles for Strategy Games [58.896302717975445]
We propose Portfolio Monte Carlo Tree Search with Progressive Unpruning for playing a turn-based strategy game (Tribes)
We show how it can be parameterized so a quality-diversity algorithm (MAP-Elites) is used to achieve different play-styles while keeping a competitive level of play.
Our results show that this algorithm is capable of achieving these goals even for an extensive collection of game levels beyond those used for training.
arXiv Detail & Related papers (2021-04-17T20:33:24Z) - Finding Game Levels with the Right Difficulty in a Few Trials through
Intelligent Trial-and-Error [16.297059109611798]
Methods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement.
Current methods often only modify a limited set of game features such as the difficulty of the opponents, or the availability of resources.
This paper presents a method that can generate and search for complete levels with a specific target difficulty in only a few trials.
arXiv Detail & Related papers (2020-05-15T17:48:18Z) - CurricularFace: Adaptive Curriculum Learning Loss for Deep Face
Recognition [79.92240030758575]
We propose a novel Adaptive Curriculum Learning loss (CurricularFace) that embeds the idea of curriculum learning into the loss function.
Our CurricularFace adaptively adjusts the relative importance of easy and hard samples during different training stages.
arXiv Detail & Related papers (2020-04-01T08:43:10Z) - Efficient exploration of zero-sum stochastic games [83.28949556413717]
We investigate the increasingly important and common game-solving setting where we do not have an explicit description of the game but only oracle access to it through gameplay.
During a limited-duration learning phase, the algorithm can control the actions of both players in order to try to learn the game and how to play it well.
Our motivation is to quickly learn strategies that have low exploitability in situations where evaluating the payoffs of a queried strategy profile is costly.
arXiv Detail & Related papers (2020-02-24T20:30:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.