4,093 research outputs found
Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms
open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarm’s inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm’s emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)
Scale-invariant cellular automata and self-similar Petri nets
Two novel computing models based on an infinite tessellation of space-time
are introduced. They consist of recursively coupled primitive building blocks.
The first model is a scale-invariant generalization of cellular automata,
whereas the second one utilizes self-similar Petri nets. Both models are
capable of hypercomputations and can, for instance, "solve" the halting problem
for Turing machines. These two models are closely related, as they exhibit a
step-by-step equivalence for finite computations. On the other hand, they
differ greatly for computations that involve an infinite number of building
blocks: the first one shows indeterministic behavior whereas the second one
halts. Both models are capable of challenging our understanding of
computability, causality, and space-time.Comment: 35 pages, 5 figure
Some Computational Aspects of Essential Properties of Evolution and Life
While evolution has inspired algorithmic methods of heuristic optimisation, little has been done in the way of using concepts of computation to advance our understanding of salient aspects of biological evolution. We argue that under reasonable assumptions, interesting conclusions can be drawn that are of relevance to behavioural evolution. We will focus on two important features of life--robustness and fitness optimisation--which, we will argue, are related to algorithmic probability and to the thermodynamics of computation, subjects that may be capable of explaining and modelling key features of living organisms, and which can be used in understanding and formulating algorithms of evolutionary computation
A guided tour of asynchronous cellular automata
Research on asynchronous cellular automata has received a great amount of
attention these last years and has turned to a thriving field. We survey the
recent research that has been carried out on this topic and present a wide
state of the art where computing and modelling issues are both represented.Comment: To appear in the Journal of Cellular Automat
Discrete and continuous time simulations of spatial ecological processes predict different final population sizes and interspecific competition outcomes
Cellular automata (CAs) are commonly used to simulate spatial processes in ecology. Although appropriate for modelling events that occur at discrete time points, they are also routinely used to model biological processes that take place continuously. We report on a study comparing predictions of discrete time CA models to those of their continuous time counterpart. Specifically, we investigate how the decision to model time discretely or continuously affects predictions regarding long-run population sizes, the probability of extinction and interspecific competition. We show effects on predicted ecological outcomes, finding quantitative differences in all cases and in the case of interspecific competition, additional qualitative differences in predictions regarding species dominance. Our findings demonstrate that qualitative conclusions drawn from spatial simulations can be critically dependent on the decision to model time discretely or continuously. Contrary to our expectations, simulating in continuous time did not incur a heavy computational penalty. We also raise ecological questions on the relative benefits of reproductive strategies that take place in discrete and continuous time
Response of electrically coupled spiking neurons: a cellular automaton approach
Experimental data suggest that some classes of spiking neurons in the first
layers of sensory systems are electrically coupled via gap junctions or
ephaptic interactions. When the electrical coupling is removed, the response
function (firing rate {\it vs.} stimulus intensity) of the uncoupled neurons
typically shows a decrease in dynamic range and sensitivity. In order to assess
the effect of electrical coupling in the sensory periphery, we calculate the
response to a Poisson stimulus of a chain of excitable neurons modeled by
-state Greenberg-Hastings cellular automata in two approximation levels. The
single-site mean field approximation is shown to give poor results, failing to
predict the absorbing state of the lattice, while the results for the pair
approximation are in good agreement with computer simulations in the whole
stimulus range. In particular, the dynamic range is substantially enlarged due
to the propagation of excitable waves, which suggests a functional role for
lateral electrical coupling. For probabilistic spike propagation the Hill
exponent of the response function is , while for deterministic spike
propagation we obtain , which is close to the experimental values
of the psychophysical Stevens exponents for odor and light intensities. Our
calculations are in qualitative agreement with experimental response functions
of ganglion cells in the mammalian retina.Comment: 11 pages, 8 figures, to appear in the Phys. Rev.
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
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Spatio-temporal patterns act as computational mechanisms governing emergent behavior in robotic swarms
Our goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their self-coordinating emergent behavior, has proven ineffective, largely due to the swarm's inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micro-macro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm's emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)
Generative Design in Minecraft (GDMC), Settlement Generation Competition
This paper introduces the settlement generation competition for Minecraft,
the first part of the Generative Design in Minecraft challenge. The settlement
generation competition is about creating Artificial Intelligence (AI) agents
that can produce functional, aesthetically appealing and believable settlements
adapted to a given Minecraft map - ideally at a level that can compete with
human created designs. The aim of the competition is to advance procedural
content generation for games, especially in overcoming the challenges of
adaptive and holistic PCG. The paper introduces the technical details of the
challenge, but mostly focuses on what challenges this competition provides and
why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018
proceedings, as part of the workshop on Procedural Content Generatio
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