Study of Computational Intelligence Algorithms to Detect Behaviour Patterns

In order to achieve the game flow and increase player retention, it is important that games difficulty matches player skills. As a consequence, to evaluate how people play a game is a crucial component, because detecting gamers strategies in video-games, it is possible to fix the game difficulty. The main problem to detect the strategies is whether attributes selected to define the strategies correctly detect the actions of the player. To study the player strategies, we will use a Real Time Stategy (RTS) game. In a RTS the players make use of units and structures to secure areas of a map and/or destroy the opponents resources. In this work, we will extract the real-time information about the players strategies using a platform base on the RTS game. After gathering information, the attributes that define the player strategies are evaluated using unsupervised learning algorithm (K-Means and Spectral Clustering). Finally, we will study the similitude among several gameplays where players use different strategies.A fin de lograr que el flujo del juego mejore y la captación de jugadores aumente, es importante que la dificultad del juego se ajuste a las habilidades del jugador. Como consecuencia, evaluar como juega la gente un juego es un aspecto importante, porque detectando las estrategias de los jugadores en los vídeo juegos, permite adapta la dificultad del juego. El problema principal para detectar las estrategias es si los atributos seleccionados para definir las estrategias definen correctamente las acciones del jugador. Para estudiar las estrategias de los jugadores, usaremos un juego de estrategia en tiempo real (Reat Time Strategy (RTS) en inglés). En un RTS los jugadores hacen uso de unidades y estructuras para asegurar áreas del mapa y/o destruir los recursos de los oponentes. En este trabajo, extraeremos información en tiempo real acerca de las estrategias usando una plataforma basada en un juego de RTS. Después de recoger la información, los atributos que definen las estrategias de los jugadores son evaluados mediante algoritmos de aprendizaje no supervisado (K-Means y Spectral Clustering). Finalmente, estudiaremos la similitud entre diversas partidas donde los jugadores utilizar diferentes estrategias.Este trabajo ha sido financiado por Airbus Defence & Space (Proyecto Savier: FUAM-076914) y parcialmente por TIN2010-19872

MAPiS 2019 - First MAP-i Seminar: proceedings

This book contains a selection of Informatics papers accepted for presentation and discussion at “MAPiS 2019 - First MAP-i Seminar”, held in Aveiro, Portugal, January 31, 2019. MAPiS is the first conference organized by the MAP-i first year students, in the context of the Seminar course. The MAP-i Doctoral Programme in Computer Science is a joint Doctoral Programme in Computer Science of the University of Minho, the University of Aveiro and the University of Porto. This programme aims to form highly-qualified professionals, fostering their capacity and knowledge to the research area. This Conference was organized by the first grade students attending the Seminar Course. The aim of the course was to introduce concepts which are complementary to scientific and technological education, but fundamental to both completing a PhD successfully and entailing a career on scientific research. The students had contact with the typical procedures and difficulties of organizing and participate in such a complex event. These students were in charge of the organization and management of all the aspects of the event, such as the accommodation of participants or revision of the papers. The works presented in the Conference and the papers submitted were also developed by these students, fomenting their enthusiasm regarding the investigation in the Informatics area. (...)publishe

The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Thiamine dose response in human milk with supplementation among lactating women in Cambodia: study protocol for a double-blind, four-parallel arm randomised controlled trial

INTRODUCTION:Thiamine (vitamin B1) deficiency remains a concern in Cambodia where women with low thiamine intake produce thiamine-poor milk, putting their breastfed infants at risk of impaired cognitive development and potentially fatal infantile beriberi. Thiamine fortification of salt is a potentially low-cost, passive means of combating thiamine deficiency; however, both the dose of thiamine required to optimise milk thiamine concentrations as well as usual salt intake of lactating women are unknown. METHODS AND ANALYSIS:In this community-based randomised controlled trial, 320 lactating women from Kampong Thom, Cambodia will be randomised to one of four groups to consume one capsule daily containing 0, 1.2, 2.4 or 10 mg thiamine as thiamine hydrochloride, between 2 and 24 weeks postnatal. The primary objective is to estimate the dose where additional maternal intake of thiamine no longer meaningfully increases infant thiamine diphosphate concentrations 24 weeks postnatally. At 2, 12 and 24 weeks, we will collect sociodemographic, nutrition and health information, a battery of cognitive assessments, maternal (2 and 24 weeks) and infant (24 weeks only) venous blood samples (biomarkers: ThDP and transketolase activity) and human milk samples (also at 4 weeks; biomarker: milk thiamine concentrations). All participants and their families will consume study-provided salt ad libitum throughout the trial, and we will measure salt disappearance each fortnight. Repeat weighed salt intakes and urinary sodium concentrations will be measured among a subset of 100 participants. Parameters of Emax dose-response curves will be estimated using non-linear least squares models with both 'intention to treat' and a secondary 'per-protocol' (capsule compliance ≥80%) analyses. ETHICS AND DISSEMINATION:Ethical approval was obtained in Cambodia (National Ethics Committee for Health Research 112/250NECHR), Canada (Mount Saint Vincent University Research Ethics Board 2017-141) and the USA (University of Oregon Institutional Review Board 07052018.008). Results will be shared with participants' communities, as well as relevant government and scientific stakeholders via presentations, academic manuscripts and consultations. TRIAL REGISTRATION NUMBER:NCT03616288.Kyly C Whitfield, Hou Kroeun, Tim Green, Frank T Wieringa, Mam Borath, Prak Sophonneary, Jeffrey R Measelle, Dare Baldwin, Lisa N Yelland, Shalem Leemaqz, Kathleen Chan, Jelisa Gallan