125 research outputs found
An Integrated Framework for AI Assisted Level Design in 2D Platformers
The design of video game levels is a complex and critical task. Levels need
to elicit fun and challenge while avoiding frustration at all costs. In this
paper, we present a framework to assist designers in the creation of levels for
2D platformers. Our framework provides designers with a toolbox (i) to create
2D platformer levels, (ii) to estimate the difficulty and probability of
success of single jump actions (the main mechanics of platformer games), and
(iii) a set of metrics to evaluate the difficulty and probability of completion
of entire levels. At the end, we present the results of a set of experiments we
carried out with human players to validate the metrics included in our
framework.Comment: Submitted to the IEEE Game Entertainment and Media Conference 201
ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game Design
Large language models (LLMs) have taken the scientific world by storm,
changing the landscape of natural language processing and human-computer
interaction. These powerful tools can answer complex questions and,
surprisingly, perform challenging creative tasks (e.g., generate code and
applications to solve problems, write stories, pieces of music, etc.). In this
paper, we present a collaborative game design framework that combines
interactive evolution and large language models to simulate the typical human
design process. We use the former to exploit users' feedback for selecting the
most promising ideas and large language models for a very complex creative task
- the recombination and variation of ideas. In our framework, the process
starts with a brief and a set of candidate designs, either generated using a
language model or proposed by the users. Next, users collaborate on the design
process by providing feedback to an interactive genetic algorithm that selects,
recombines, and mutates the most promising designs. We evaluated our framework
on three game design tasks with human designers who collaborated remotely.Comment: (Submitted
Volcano: An interactive sword generator
In this work, we introduce Volcano, a tool for the procedural generation of 3D models of swords. Unlike common procedural content generation tools, it exploits interactive evolution to reduce as much as possible the effort of the users during the generation process. Indeed, Volcano allows to forge the desired type of swords through a rather simple visual exploration of the design space. The 3D models generated with the tool can be directly used as game assets or further developed with a standard modeling software. A prototype of Volcano was tested by 30 users, including both students and game developers. The feedbacks received are very positive: tools like Volcano might be useful both for players, to create user contents, and for developers, to speed-up the design of game contents
Chest X-Rays Image Classification from beta-Variational Autoencoders Latent Features
Chest X-Ray (CXR) is one of the most common diagnostic techniques used in
everyday clinical practice all around the world. We hereby present a work which
intends to investigate and analyse the use of Deep Learning (DL) techniques to
extract information from such images and allow to classify them, trying to keep
our methodology as general as possible and possibly also usable in a real world
scenario without much effort, in the future. To move in this direction, we
trained several beta-Variational Autoencoder (beta-VAE) models on the CheXpert
dataset, one of the largest publicly available collection of labeled CXR
images; from these models, latent features have been extracted and used to
train other Machine Learning models, able to classify the original images from
the features extracted by the beta-VAE. Lastly, tree-based models have been
combined together in ensemblings to improve the results without the necessity
of further training or models engineering. Expecting some drop in pure
performance with the respect to state of the art classification specific
models, we obtained encouraging results, which show the viability of our
approach and the usability of the high level features extracted by the
autoencoders for classification tasks.Comment: 8 pages, 5 figure
Brain MRI Tumor Segmentation with Adversarial Networks
Deep Learning is a promising approach to either automate or simplify several
tasks in the healthcare domain. In this work, we introduce SegAN-CAT, an
approach to brain tumor segmentation in Magnetic Resonance Images (MRI), based
on Adversarial Networks. In particular, we extend SegAN, successfully applied
to the same task in a previous work, in two respects: (i) we used a different
model input and (ii) we employed a modified loss function to train the model.
We tested our approach on two large datasets, made available by the Brain Tumor
Image Segmentation Benchmark (BraTS). First, we trained and tested some
segmentation models assuming the availability of all the major MRI contrast
modalities, i.e., T1-weighted, T1 weighted contrast-enhanced, T2-weighted, and
T2-FLAIR. However, as these four modalities are not always all available for
each patient, we also trained and tested four segmentation models that take as
input MRIs acquired only with a single contrast modality. Finally, we proposed
to apply transfer learning across different contrast modalities to improve the
performance of these single-modality models. Our results are promising and show
that not SegAN-CAT is able to outperform SegAN when all the four modalities are
available, but also that transfer learning can actually lead to better
performances when only a single modality is available
Comparing Adversarial and Supervised Learning for Organs at Risk Segmentation in CT images
Organ at Risk (OAR) segmentation from CT scans is a key component of the
radiotherapy treatment workflow. In recent years, deep learning techniques have
shown remarkable potential in automating this process. In this paper, we
investigate the performance of Generative Adversarial Networks (GANs) compared
to supervised learning approaches for segmenting OARs from CT images. We
propose three GAN-based models with identical generator architectures but
different discriminator networks. These models are compared with
well-established CNN models, such as SE-ResUnet and DeepLabV3, using the
StructSeg dataset, which consists of 50 annotated CT scans containing contours
of six OARs. Our work aims to provide insight into the advantages and
disadvantages of adversarial training in the context of OAR segmentation. The
results are very promising and show that the proposed GAN-based approaches are
similar or superior to their CNN-based counterparts, particularly when
segmenting more challenging target organs
Player Modeling
Player modeling is the study of computational models of players in games. This includes the detection, modeling, prediction and expression of human player characteristics which are manifested
through cognitive, affective and behavioral patterns. This chapter introduces a holistic view of player modeling and provides a high level taxonomy and discussion of the key components of a player\u27s model. The discussion focuses on a taxonomy of approaches for constructing a player model, the available types of data for the model\u27s input and a proposed classification for the model\u27s output. The chapter provides also a brief overview of some promising applications and a discussion of the key challenges player modeling is currently facing which are linked to the input, the output and the computational model
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