5,621 research outputs found
Multi-level agent-based modeling - A literature survey
During last decade, multi-level agent-based modeling has received significant
and dramatically increasing interest. In this article we present a
comprehensive and structured review of literature on the subject. We present
the main theoretical contributions and application domains of this concept,
with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic
statistics updated. v7 Change of the name of the paper to reflect what it
became, many refs and text added, bibliographic statistics update
Modeling rationality to control self-organization of crowds: An environmental approach
In this paper we propose a classification of crowd models in built
environments based on the assumed pedestrian ability to foresee the movements
of other walkers. At the same time, we introduce a new family of macroscopic
models, which make it possible to tune the degree of predictiveness (i.e.,
rationality) of the individuals. By means of these models we describe both the
natural behavior of pedestrians, i.e., their expected behavior according to
their real limited predictive ability, and a target behavior, i.e., a
particularly efficient behavior one would like them to assume (for, e.g.,
logistic or safety reasons). Then we tackle a challenging shape optimization
problem, which consists in controlling the environment in such a way that the
natural behavior is as close as possible to the target one, thereby inducing
pedestrians to behave more rationally than what they would naturally do. We
present numerical tests which elucidate the role of rational/predictive
abilities and show some promising results about the shape optimization problem
End-to-end Learning of Driving Models from Large-scale Video Datasets
Robust perception-action models should be learned from training data with
diverse visual appearances and realistic behaviors, yet current approaches to
deep visuomotor policy learning have been generally limited to in-situ models
learned from a single vehicle or a simulation environment. We advocate learning
a generic vehicle motion model from large scale crowd-sourced video data, and
develop an end-to-end trainable architecture for learning to predict a
distribution over future vehicle egomotion from instantaneous monocular camera
observations and previous vehicle state. Our model incorporates a novel
FCN-LSTM architecture, which can be learned from large-scale crowd-sourced
vehicle action data, and leverages available scene segmentation side tasks to
improve performance under a privileged learning paradigm.Comment: camera ready for CVPR201
From individual behaviour to an evaluation of the collective evolution of crowds along footbridges
This paper proposes a crowd dynamic macroscopic model grounded on microscopic
phenomenological observations which are upscaled by means of a formal
mathematical procedure. The actual applicability of the model to real world
problems is tested by considering the pedestrian traffic along footbridges, of
interest for Structural and Transportation Engineering. The genuinely
macroscopic quantitative description of the crowd flow directly matches the
engineering need of bulk results. However, three issues beyond the sole
modelling are of primary importance: the pedestrian inflow conditions, the
numerical approximation of the equations for non trivial footbridge geometries,
and the calibration of the free parameters of the model on the basis of in situ
measurements currently available. These issues are discussed and a solution
strategy is proposed.Comment: 23 pages, 10 figures in J. Engrg. Math., 201
Modeling self-organization in pedestrians and animal groups from macroscopic and microscopic viewpoints
This paper is concerned with mathematical modeling of intelligent systems,
such as human crowds and animal groups. In particular, the focus is on the
emergence of different self-organized patterns from non-locality and anisotropy
of the interactions among individuals. A mathematical technique by
time-evolving measures is introduced to deal with both macroscopic and
microscopic scales within a unified modeling framework. Then self-organization
issues are investigated and numerically reproduced at the proper scale,
according to the kind of agents under consideration.Comment: 24 pages, 13 figure
A Comprehensive Study on Pedestrians' Evacuation
Human beings face threats because of unexpected happenings, which can be
avoided through an adequate crisis evacuation plan, which is vital to stop
wound and demise as its negative results. Consequently, different typical
evacuation pedestrians have been created. Moreover, through applied research,
these models for various applications, reproductions, and conditions have been
examined to present an operational model. Furthermore, new models have been
developed to cooperate with system evacuation in residential places in case of
unexpected events. This research has taken into account an inclusive and a
'systematic survey of pedestrian evacuation' to demonstrate models methods by
focusing on the applications' features, techniques, implications, and after
that gather them under various types, for example, classical models, hybridized
models, and generic model. The current analysis assists scholars in this field
of study to write their forthcoming papers about it, which can suggest a novel
structure to recent typical intelligent reproduction with novel features
Toward Realism in Human Performance Simulation
This chapter focuses on challenges to improving the realism of socially intelligent agents and attempts to reflect the state of the art in human behavior modeling with particular attention to the impact of values, emotion, and physiology/stress upon individual and group decision-making. The goal is to help those interested in constructing more realistic software agents for use in human performance simulations in both training and analysis settings. The first two sections offer an assessment of the state of the practice and of the need to make better use of human performance moderator functions (PMFs) published in the behavioral literature. The third section pursues this goal by providing an illustrative framework for integrating existing PMF theories and models, such as those on physiology and stress, cognitive and emotive processes, individual differences, and group and crowd behavior, among others. The fourth section presents asymmetric warfare and civil unrest case studies to examine some of the concerns affecting implementation of PMFs such as verification, validation, and interoperability with existing simulators, artificial life emulators, and artificial intelligence components. The final section of this chapter concludes with lessons learned and with some challenges if the field is to reach a greater level of maturity
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