535 research outputs found
An Agent-Based Approach to Self-Organized Production
The chapter describes the modeling of a material handling system with the
production of individual units in a scheduled order. The units represent the
agents in the model and are transported in the system which is abstracted as a
directed graph. Since the hindrances of units on their path to the destination
can lead to inefficiencies in the production, the blockages of units are to be
reduced. Therefore, the units operate in the system by means of local
interactions in the conveying elements and indirect interactions based on a
measure of possible hindrances. If most of the units behave cooperatively
("socially"), the blockings in the system are reduced.
A simulation based on the model shows the collective behavior of the units in
the system. The transport processes in the simulation can be compared with the
processes in a real plant, which gives conclusions about the consequencies for
the production based on the superordinate planning.Comment: For related work see http://www.soms.ethz.c
Informative and misinformative interactions in a school of fish
It is generally accepted that, when moving in groups, animals process
information to coordinate their motion. Recent studies have begun to apply
rigorous methods based on Information Theory to quantify such distributed
computation. Following this perspective, we use transfer entropy to quantify
dynamic information flows locally in space and time across a school of fish
during directional changes around a circular tank, i.e. U-turns. This analysis
reveals peaks in information flows during collective U-turns and identifies two
different flows: an informative flow (positive transfer entropy) based on fish
that have already turned about fish that are turning, and a misinformative flow
(negative transfer entropy) based on fish that have not turned yet about fish
that are turning. We also reveal that the information flows are related to
relative position and alignment between fish, and identify spatial patterns of
information and misinformation cascades. This study offers several
methodological contributions and we expect further application of these
methodologies to reveal intricacies of self-organisation in other animal groups
and active matter in general
Multi-scale analysis and modelling of collective migration in biological systems
Collective migration has become a paradigm for emergent behaviour in systems of moving and interacting individual units resulting in coherent motion. In biology, these units are cells or organisms. Collective cell migration is important in embryonic development, where it underlies tissue and organ formation, as well as pathological processes, such as cancer invasion and metastasis. In animal groups, collective movements may enhance individuals' decisions and facilitate navigation through complex environments and access to food resources. Mathematical models can extract unifying principles behind the diverse manifestations of collective migration. In biology, with a few exceptions, collective migration typically occurs at a 'mesoscopic scale' where the number of units ranges from only a few dozen to a few thousands, in contrast to the large systems treated by statistical mechanics. Recent developments in multi-scale analysis have allowed linkage of mesoscopic to micro- and macroscopic scales, and for different biological systems. The articles in this theme issue on 'Multi-scale analysis and modelling of collective migration' compile a range of mathematical modelling ideas and multi-scale methods for the analysis of collective migration. These approaches (i) uncover new unifying organization principles of collective behaviour, (ii) shed light on the transition from single to collective migration, and (iii) allow us to define similarities and differences of collective behaviour in groups of cells and organisms. As a common theme, self-organized collective migration is the result of ecological and evolutionary constraints both at the cell and organismic levels. Thereby, the rules governing physiological collective behaviours also underlie pathological processes, albeit with different upstream inputs and consequences for the group. This article is part of the theme issue 'Multi-scale analysis and modelling of collective migration in biological systems'
Vision-based macroscopic pedestrian models
International audienceWe propose a hierarchy of kinetic and macroscopic models for a system consisting of a large number of interacting pedestrians. The basic interaction rules are derived from earlier work where the dangerousness level of an interaction with another pedestrian is measured in terms of the derivative of the bearing angle (angle between the walking direction and the line connecting the two subjects) and of the time-to-interaction (time before reaching the closest distance between the two subjects). A mean-field kinetic model is derived. Then, three different macroscopic continuum models are proposed. The first two ones rely on two different closure assumptions of the kinetic model, respectively based on a monokinetic and a von Mises-Fisher distribution. The third one is derived through a hydrodynamic limit. In each case, we discuss the relevance of the model for practical simulations of pedestrian crowds
How simple rules determine pedestrian behavior and crowd disasters
With the increasing size and frequency of mass events, the study of crowd
disasters and the simulation of pedestrian flows have become important research
areas. Yet, even successful modeling approaches such as those inspired by
Newtonian force models are still not fully consistent with empirical
observations and are sometimes hard to calibrate. Here, a novel cognitive
science approach is proposed, which is based on behavioral heuristics. We
suggest that, guided by visual information, namely the distance of obstructions
in candidate lines of sight, pedestrians apply two simple cognitive procedures
to adapt their walking speeds and directions. While simpler than previous
approaches, this model predicts individual trajectories and collective patterns
of motion in good quantitative agreement with a large variety of empirical and
experimental data. This includes the emergence of self-organization phenomena,
such as the spontaneous formation of unidirectional lanes or stop-and-go waves.
Moreover, the combination of pedestrian heuristics with body collisions
generates crowd turbulence at extreme densities-a phenomenon that has been
observed during recent crowd disasters. By proposing an integrated treatment of
simultaneous interactions between multiple individuals, our approach overcomes
limitations of current physics-inspired pair interaction models. Understanding
crowd dynamics through cognitive heuristics is therefore not only crucial for a
better preparation of safe mass events. It also clears the way for a more
realistic modeling of collective social behaviors, in particular of human
crowds and biological swarms. Furthermore, our behavioral heuristics may serve
to improve the navigation of autonomous robots.Comment: Article accepted for publication in PNA
Quantifying the biomimicry gap in biohybrid systems
Biohybrid systems in which robotic lures interact with animals have become
compelling tools for probing and identifying the mechanisms underlying
collective animal behavior. One key challenge lies in the transfer of social
interaction models from simulations to reality, using robotics to validate the
modeling hypotheses. This challenge arises in bridging what we term the
"biomimicry gap", which is caused by imperfect robotic replicas, communication
cues and physics constrains not incorporated in the simulations that may elicit
unrealistic behavioral responses in animals. In this work, we used a biomimetic
lure of a rummy-nose tetra fish (Hemigrammus rhodostomus) and a neural network
(NN) model for generating biomimetic social interactions. Through experiments
with a biohybrid pair comprising a fish and the robotic lure, a pair of real
fish, and simulations of pairs of fish, we demonstrate that our biohybrid
system generates high-fidelity social interactions mirroring those of genuine
fish pairs. Our analyses highlight that: 1) the lure and NN maintain minimal
deviation in real-world interactions compared to simulations and fish-only
experiments, 2) our NN controls the robot efficiently in real-time, and 3) a
comprehensive validation is crucial to bridge the biomimicry gap, ensuring
realistic biohybrid systems
A knowledge-based view of the extending enterprise for enhancing a collaborative innovation advantage
In animal societies as well as in human crowds, many observed collective
behaviours result from self-organized processes based on local interactions
among individuals. However, models of crowd dynamics are still lacking a
systematic individual-level experimental verification, and the local mechanisms
underlying the formation of collective patterns are not yet known in detail. We
have conducted a set of well-controlled experiments with pedestrians performing
simple avoidance tasks in order to determine the laws ruling their behaviour
during interactions. The analysis of the large trajectory dataset was used to
compute a behavioural map that describes the average change of the direction
and speed of a pedestrian for various interaction distances and angles. The
experimental results reveal features of the decision process when pedestrians
choose the side on which they evade, and show a side preference that is
amplified by mutual interactions. The predictions of a binary interaction model
based on the above findings were then compared to bidirectional flows of people
recorded in a crowded street. Simulations generate two asymmetric lanes with
opposite directions of motion, in quantitative agreement with our empirical
observations. The knowledge of pedestrian behavioural laws is an important step
ahead in the understanding of the underlying dynamics of crowd behaviour and
allows for reliable predictions of collective pedestrian movements under
natural conditions
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