44,328 research outputs found
Roborobo! a Fast Robot Simulator for Swarm and Collective Robotics
Roborobo! is a multi-platform, highly portable, robot simulator for
large-scale collective robotics experiments. Roborobo! is coded in C++, and
follows the KISS guideline ("Keep it simple"). Therefore, its external
dependency is solely limited to the widely available SDL library for fast 2D
Graphics. Roborobo! is based on a Khepera/ePuck model. It is targeted for fast
single and multi-robots simulation, and has already been used in more than a
dozen published research mainly concerned with evolutionary swarm robotics,
including environment-driven self-adaptation and distributed evolutionary
optimization, as well as online onboard embodied evolution and embodied
morphogenesis.Comment: 2 pages, 1 figur
Agent Street: An Environment for Exploring Agent-Based Models in Second Life
Urban models can be seen on a continuum between iconic and symbolic. Generally speaking, iconic models are physical versions of the real world at some scaled down representation, while symbolic models represent the system in terms of the way they function replacing the physical or material system by some logical and/or mathematical formulae. Traditionally iconic and symbolic models were distinct classes of model but due to the rise of digital computing the distinction between the two is becoming blurred, with symbolic models being embedded into iconic models. However, such models tend to be single user. This paper demonstrates how 3D symbolic models in the form of agent-based simulations can be embedded into iconic models using the multi-user virtual world of Second Life. Furthermore, the paper demonstrates Second Life\'s potential for social science simulation. To demonstrate this, we first introduce Second Life and provide two exemplar models; Conway\'s Game of Life, and Schelling\'s Segregation Model which highlight how symbolic models can be viewed in an iconic environment. We then present a simple pedestrian evacuation model which merges the iconic and symbolic together and extends the model to directly incorporate avatars and agents in the same environment illustrating how \'real\' participants can influence simulation outcomes. Such examples demonstrate the potential for creating highly visual, immersive, interactive agent-based models for social scientists in multi-user real time virtual worlds. The paper concludes with some final comments on problems with representing models in current virtual worlds and future avenues of research.Agent-Based Modelling, Pedestrian Evacuation, Segregation, Virtual Worlds, Second Life
The Self-Organization of Speech Sounds
The speech code is a vehicle of language: it defines
a set of forms used by a community to carry information.
Such a code is necessary to support the linguistic
interactions that allow humans to communicate.
How then may a speech code be formed prior to the
existence of linguistic interactions?
Moreover, the human speech code is discrete and compositional,
shared by all the individuals of a community but different
across communities, and phoneme inventories are characterized by
statistical regularities. How can a speech code with these properties form?
We try to approach these questions in the paper,
using the ``methodology of the artificial''. We
build a society of artificial agents, and detail a mechanism that
shows the formation of a discrete speech code without pre-supposing
the existence of linguistic capacities or of coordinated interactions.
The mechanism is based on a low-level model of
sensory-motor interactions. We show that the integration of certain very
simple and non language-specific neural devices
leads to the formation of a speech code that
has properties similar to the human speech code.
This result relies on the self-organizing properties of a generic
coupling between perception and production
within agents, and on the interactions between agents.
The artificial system helps us to develop better intuitions on how speech
might have appeared, by showing how self-organization
might have helped natural selection to find speech
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
Ab Initio Modeling of Ecosystems with Artificial Life
Artificial Life provides the opportunity to study the emergence and evolution
of simple ecosystems in real time. We give an overview of the advantages and
limitations of such an approach, as well as its relation to individual-based
modeling techniques. The Digital Life system Avida is introduced and prospects
for experiments with ab initio evolution (evolution "from scratch"),
maintenance, as well as stability of ecosystems are discussed.Comment: 13 pages, 2 figure
Evolution of swarming behavior is shaped by how predators attack
Animal grouping behaviors have been widely studied due to their implications
for understanding social intelligence, collective cognition, and potential
applications in engineering, artificial intelligence, and robotics. An
important biological aspect of these studies is discerning which selection
pressures favor the evolution of grouping behavior. In the past decade,
researchers have begun using evolutionary computation to study the evolutionary
effects of these selection pressures in predator-prey models. The selfish herd
hypothesis states that concentrated groups arise because prey selfishly attempt
to place their conspecifics between themselves and the predator, thus causing
an endless cycle of movement toward the center of the group. Using an
evolutionary model of a predator-prey system, we show that how predators attack
is critical to the evolution of the selfish herd. Following this discovery, we
show that density-dependent predation provides an abstraction of Hamilton's
original formulation of ``domains of danger.'' Finally, we verify that
density-dependent predation provides a sufficient selective advantage for prey
to evolve the selfish herd in response to predation by coevolving predators.
Thus, our work corroborates Hamilton's selfish herd hypothesis in a digital
evolutionary model, refines the assumptions of the selfish herd hypothesis, and
generalizes the domain of danger concept to density-dependent predation.Comment: 25 pages, 11 figures, 5 tables, including 2 Supplementary Figures.
Version to appear in "Artificial Life
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