Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems

Predictive Markers of Honey Bee Colony Collapse

Across the Northern hemisphere, managed honey bee colonies, Apis mellifera, are currently affected by abrupt depopulation during winter and many factors are suspected to be involved, either alone or in combination. Parasites and pathogens are considered as principal actors, in particular the ectoparasitic mite Varroa destructor, associated viruses and the microsporidian Nosema ceranae. Here we used long term monitoring of colonies and screening for eleven disease agents and genes involved in bee immunity and physiology to identify predictive markers of honeybee colony losses during winter. The data show that DWV, Nosema ceranae, Varroa destructor and Vitellogenin can be predictive markers for winter colony losses, but their predictive power strongly depends on the season. In particular, the data support that V. destructor is a key player for losses, arguably in line with its specific impact on the health of individual bees and colonies

DOOM Level Generation using Generative Adversarial Networks

We applied Generative Adversarial Networks (GANs) to learn a model of DOOM levels from human-designed content. Initially, we analyzed 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 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

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

Serious Games for Wrist Rehabilitation in Juvenile Idiopathic Arthritis

Rehabilitation is a painful and tiring process involving series of exercises that patients must repeat over a long period. Unfortunately, patients often grow bored, frustrated, and lose motivation making rehabilitation less effective. In the recent years video games have been widely used to implement rehabilitation protocols so as to make the process more entertaining, engaging and to keep patients motivated. In this paper, we present an integrated framework we developed for the wrist rehabilitation of patients affected by Juvenile Idiopathic Arthritis (JIA) following a therapeutic protocol at the Clinica Pediatrica G. e D. De Marchi. The framework comprises four video games and a set modules that let the therapists tune and control the exercises the games implemented, record all the patients actions, replay and analyze the sessions. We present the result of a preliminary validation we performed with four poliarticular JIA patients at the clinic under the supervision of the therapists. Overall, we received good feedback both from the young patients, who enjoyed performing known rehabilitation exercises using video games, and therapists who were satisfied with the framework and its potentials for engaging and motivating the patients

One-sided instance-based boundary sets

Instance retraction is a difficult problem for concept learning by version spaces. This chapter introduces a family of version-space representations called one-sided instance-based boundary sets. They are correct and efficiently computable representations for admissible concept languages. Compared to other representations, they are the most efficient useful(1) version-space representations for instance retraction