9,659 research outputs found

    Challenges in Complex Systems Science

    Get PDF
    FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

    Agents for educational games and simulations

    Get PDF
    This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

    Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

    Full text link
    Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table

    The 1990 progress report and future plans

    Get PDF
    This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

    Autonomous exploration of hierarchical scene graphs

    Get PDF
    L'exploració robòtica autònoma és un camp de recerca actiu, on els mètodes de percepció robòtica hi abunden. Els mètodes basats en grafs, en particular, són una manera de representar l'entorn de forma eficient, i ofereixen una base sobre la que raonar a alt nivell per resoldre tasques de l'àmbit de la robòtica. Proposem un sistema per generar grafs jeràrquics d'escena automàticament a partir d'entorns foto-realistes. En aquest treball emprem un mètode de percepció basat en grafs, Hydra, en combinació amb un simulador 3D anomenat Habitat-Sim, per explorar i generar representacions en forma de grafs d'escena 3D dels entorns tridimensionals simulats. Aquest sistema i les dades que n'han derivat ens donen una base sobre la que establim un mètode general per resoldre tasques d'exploració en entorns tridimensionals mitjançant Xarxes Neuronals per a Grafs i Aprenentatge per Reforç.La exploración robótica autónoma es un campo de investigación activo, donde los métodos de percepción robótica abundan. Los métodos basados en grafos, en particular, son una forma de representar el entorno de forma eficiente, y ofrecen una base sobre la que razonar a alto nivel para resolver tareas del ámbito de la robótica. Proponemos un sistema para generar grafos jerárquicos de escena automáticamente a partir de entornos fotorealistas. En este trabajo usamos un método de percepción basado en grafos, Hydra, en combinación con un simulador 3D llamado Habitat-Sim, para explorar y generar representaciones en forma de grafos de escena 3D de los entornos tridimensionales simulados. Este sistema y los datos que han derivado de él nos dan una base sobre la que establecemos un método general para resolver tareas de exploración en entornos tridimensionales mediante Redes Neuronales para Grafos y Aprendizaje por Refuerzo.Robotic autonomous exploration is an active field of research, where robot perception pipelines abound. Graph-based pipelines, in particular, are a way to represent the environment efficiently, and provide grounds for reasoning on a high level to solve robotics tasks. We propose a framework to generate hierarchical scene graphs automatically from photo-realistic environments. In this thesis, a graph perception pipeline, Hydra, is employed in combination with Habitat-Sim, a 3D simulator, to explore and generate 3D scene graph representations from the simulated 3D maps. This framework and data have provided the grounds to establish a general pipeline for solving exploration tasks in 3D environments using Graph Neural Networks and Reinforcement Learning.Outgoin

    Multi-agent evolutionary systems for the generation of complex virtual worlds

    Full text link
    Modern films, games and virtual reality applications are dependent on convincing computer graphics. Highly complex models are a requirement for the successful delivery of many scenes and environments. While workflows such as rendering, compositing and animation have been streamlined to accommodate increasing demands, modelling complex models is still a laborious task. This paper introduces the computational benefits of an Interactive Genetic Algorithm (IGA) to computer graphics modelling while compensating the effects of user fatigue, a common issue with Interactive Evolutionary Computation. An intelligent agent is used in conjunction with an IGA that offers the potential to reduce the effects of user fatigue by learning from the choices made by the human designer and directing the search accordingly. This workflow accelerates the layout and distribution of basic elements to form complex models. It captures the designer's intent through interaction, and encourages playful discovery
    corecore