Filters








204 Hits in 1.0 sec

DOOM Level Generation using Generative Adversarial Networks [article]

Edoardo Giacomello and Pier Luca Lanzi and Daniele Loiacono
2018 arXiv   pre-print
We applied Generative Adversarial Networks (GANs) to learn a model of DOOM levels from human-designed content. Initially, we analysed the levels and extracted several topological features. Then, for each level, we extracted a set of images identifying the occupied area, the height map, the walls, and the position of game objects. We trained two GANs: one using plain level images, one using both the images and some of the features extracted during the preliminary analysis. We used the two
more » ... used the two networks to generate new levels and compared the results to assess whether the network trained using also the topological features could generate levels more similar to human-designed ones. Our results show that GANs can capture intrinsic structure of DOOM levels and appears to be a promising approach to level generation in first person shooter games.
arXiv:1804.09154v1 fatcat:obshz7smxnarlnbl3eplzwydti

Procedural weapons generation for unreal tournament III

Daniele Gravina, Daniele Loiacono
2015 2015 IEEE Games Entertainment Media Conference (GEM)  
Design a set of weapons for a competitive first person shooter (FPS) is not an easy task: it involves several challenges, such as balancing, diversity, and novelty. In this paper, we combine procedural content generation and evolutionary computation to solve this complex task. In particular, we introduce a novel approach to procedurally generate weapons for Unreal Tournament III, a popular commercial FPS. In our approach, each weapon is represented as a parameters vector and evaluated based on
more » ... evaluated based on game statistics. We tested our approach on three different application scenarios that represents three typical design problems: (i) create a balanced set of weapons, (ii) improve a given weapon with the least possible changes, and (iii) create a set of weapons with some specific design goals. Finally, we also performed a preliminary user study to validate our work. Our results are promising and suggest that the proposed approach might be successfully used to support the weapon design process.
doi:10.1109/gem.2015.7377225 dblp:conf/gamesem/GravinaL15 fatcat:kqxtcgn6undsxn4d5hnuwre5ze

Distributed Learning Approaches for Automated Chest X-Ray Diagnosis [article]

Edoardo Giacomello, Michele Cataldo, Daniele Loiacono, Pier Luca Lanzi
2021 arXiv   pre-print
Deep Learning has established in the latest years as a successful approach to address a great variety of tasks. Healthcare is one of the most promising field of application for Deep Learning approaches since it would allow to help clinicians to analyze patient data and perform diagnoses. However, despite the vast amount of data collected every year in hospitals and other clinical institutes, privacy regulations on sensitive data - such as those related to health - pose a serious challenge to
more » ... ous challenge to the application of these methods. In this work, we focus on strategies to cope with privacy issues when a consortium of healthcare institutions needs to train machine learning models for identifying a particular disease, comparing the performances of two recent distributed learning approaches - Federated Learning and Split Learning - on the task of Automated Chest X-Ray Diagnosis. In particular, in our analysis we investigated the impact of different data distributions in client data and the possible policies on the frequency of data exchange between the institutions.
arXiv:2110.01474v1 fatcat:e4rdmpbqinejdk24s4ftav3spq

Chest X-Rays Image Classification from beta-Variational Autoencoders Latent Features [article]

Leonardo Crespi, Daniele Loiacono, Arturo Chiti
2021 arXiv   pre-print
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
more » ... ral 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.
arXiv:2109.14760v1 fatcat:yytuaewagbea7l63ertd6lzppy

Tile Coding Based on Hyperplane Tiles [chapter]

Daniele Loiacono, Pier Luca Lanzi
2008 Lecture Notes in Computer Science  
In large and continuous state-action spaces reinforcement learning heavily relies on function approximation techniques. Tile coding is a well-known function approximator that has been successfully applied to many reinforcement learning tasks. In this paper we introduce the hyperplane tile coding, in which the usual tiles are replaced by parameterized hyperplanes that approximate the action-value function. We compared the performance of hyperplane tile coding with the usual tile coding on three
more » ... le coding on three well-known benchmark problems. Our results suggest that the hyperplane tiles improve the generalization capabilities of the tile coding approximator: in the hyperplane tile coding broad generalizations over the problem space result only in a soft degradation of the performance, whereas in the usual tile coding they might dramatically affect the performance.
doi:10.1007/978-3-540-89722-4_14 fatcat:n6f7h5bbrzhnzee4itpa5wuxjq

Special issue on GECCO competitions

Daniele Loiacono, Moshe Sipper
2014 Genetic Programming and Evolvable Machines  
Loiacono and Pier Luca Lanzi.  ...  . • The GPUs for Genetic and Evolutionary Computation competition, organized by Daniele Loiacono and Antonino Tumeo, focused on the application of genetic and evolutionary computation that can maximally  ... 
doi:10.1007/s10710-014-9226-0 fatcat:g33resflefdvfpzn4c4i5ifzau

Volcano: An interactive sword generator

Daniele Loiacono, Renato Mainetti, Michele Pirovano
2015 2015 IEEE Games Entertainment Media Conference (GEM)  
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
more » ... oped 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. 1
doi:10.1109/gem.2015.7377226 dblp:conf/gamesem/PirovanoML15 fatcat:pwimha7s3bhldjr3ayc2b3wboa

An Integrated Framework for AI Assisted Level Design in 2D Platformers [article]

Antonio Umberto Aramini, Pier Luca Lanzi, Daniele Loiacono
2018 arXiv   pre-print
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
more » ... ics 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.
arXiv:1804.09153v1 fatcat:wrihaa7vdzgnlbng3qlit3hy4y

Simulated Car Racing Championship: Competition Software Manual [article]

Daniele Loiacono and Luigi Cardamone and Pier Luca Lanzi
2013 arXiv   pre-print
This manual describes the competition software for the Simulated Car Racing Championship, an international competition held at major conferences in the field of Evolutionary Computation and in the field of Computational Intelligence and Games. It provides an overview of the architecture, the instructions to install the software and to run the simple drivers provided in the package, the description of the sensors and the actuators.
arXiv:1304.1672v2 fatcat:bluwbxb55bacte6djahsd3mrq4

XCSF with tile coding in discontinuous action-value landscapes

Pier Luca Lanzi, Daniele Loiacono
2015 Evolutionary Intelligence  
Tile coding is an effective reinforcement learning method that uses a rather ingenious generalization mechanism based on (i) a carefully designed parameter setting and (ii) the assumption that nearby states in the problem space will correspond to similar payoff predictions in the action-value function. Previously, we extended XCSF with tile coding prediction and compared it to tabular tile coding, showing that (i) XCSF performs as well as parameter-optimized tile coding, while also (ii)
more » ... e also (ii) evolving individualized parameter settings in each problem subspace. Our comparison was based on a set of well-known reinforcement learning environments (2D Gridworld and the Mountain Car) that involved no action-value discontinuities and so posed no challenge to tabular tile coding.
doi:10.1007/s12065-015-0129-7 fatcat:jlrbw2xgnbfo3cnxpz6c36rtki

Special issue on advances in learning classifier systems

Daniele Loiacono, Albert Orriols-Puig, Ryan Urbanowicz
2012 Evolutionary Intelligence  
doi:10.1007/s12065-012-0081-8 fatcat:xq6mnmtyeffptg7y4giufpgji4

Brain MRI Tumor Segmentation with Adversarial Networks [article]

Edoardo Giacomello, Daniele Loiacono, Luca Mainardi
2020 arXiv   pre-print
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
more » ... roach 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.
arXiv:1910.02717v2 fatcat:bf4zwamknbeijgp56rr6f4fj5e

Recursive Least Squares and Quadratic Prediction in Continuous Multistep Problems [chapter]

Daniele Loiacono, Pier Luca Lanzi
2010 Lecture Notes in Computer Science  
XCS with computed prediction, namely XCSF, has been recently extended in several ways. In particular, a novel prediction update algorithm based on recursive least squares and the extension to polynomial prediction led to significant improvements of XCSF. However, these extensions have been studied so far only on single step problems and it is currently not clear if these findings might be extended also to multistep problems. In this paper we investigate this issue by analyzing the performance
more » ... g the performance of XCSF with recursive least squares and with quadratic prediction on continuous multistep problems. Our results show that both these extensions improve the convergence speed of XCSF toward an optimal performance. As showed by the analysis reported in this paper, these improvements are due to the capabilities of recursive least squares and of polynomial prediction to provide a more accurate approximation of the problem value function after the first few learning problems.
doi:10.1007/978-3-642-17508-4_6 fatcat:y6ab7wss6rffphl6hrf4xlxsyy

TrackGen: An interactive track generator for TORCS and Speed-Dreams

Luigi Cardamone, Pier Luca Lanzi, Daniele Loiacono
2015 Applied Soft Computing  
TrackGen is an on-line tool for the generation of tracks for two open-source 3D car racing games (TORCS and Speed Dreams). It integrates interactive evolution with procedural content generation and comprises two components: (i) a web frontend that maintains the database of all the evolved populations and manages the interaction with users (by collecting users evaluations and providing access to the evolved tracks); (ii) an evolutionary/contentgeneration backend that runs both the evolutionary
more » ... the evolutionary algorithm and generates the actual game content that is available through the web frontend. The
doi:10.1016/j.asoc.2014.11.010 fatcat:vgo2saanwjfptiqxo5glbc5yiu

Generalization in the XCSF Classifier System: Analysis, Improvement, and Extension

Pier Luca Lanzi, Daniele Loiacono, Stewart W. Wilson, David E. Goldberg
2007 Evolutionary Computation  
, Loiacono, Wilson, and Goldberg 2005b) .  ...  (Lanzi, Loiacono, Wilson, and Goldberg 2005b ). 2 Description of XCSF XCSF (Wilson 2001a; Wilson 2002 ) is a model of learning classifier system that extends the typical concept of classifiers through  ... 
doi:10.1162/evco.2007.15.2.133 pmid:17535137 fatcat:3yw337woaja5rmqptzblcgln54
« Previous Showing results 1 — 15 out of 204 results