1,972 research outputs found
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
Simulating emergent urban form: desakota in China
We propose that the emergent phenomenon know as ?desakota?, the rapidurbanization of densely populated rural populations in the newlydeveloped world, particularly China, can be simulated using agent-basedmodels which combine both local and global features. We argue thatdeskota represents a surprising and unusual form of urbanization wellmatchedto processes of land development that are driven from the bottomup but moderated by the higher-level macro economy. We develop asimple logic which links local household reform to global urban reform,translating these ideas into a model structure which reflects these twoscales. Our model first determines the rate of growth of different spatialaggregates using linear statistical analysis. It then allocates this growth tothe local level using developer agents who determine the transformation ormutation of rural households to urban pursuits based on local land costs,accessibilities, and growth management practices. The model is applied todesakota development in the Suzhou region between 1990 and 2000. Weshow how the global rates of change predicted at the township level in theWuxian City region surrounding Suzhou are tempered by localtransformations of rural to urban land uses which we predict using cellularautomata rules. The model, which is implemented in the RePast 3software, is validated using a blend of data taken from remote sensing andgovernment statistical sources. It represents an example of generativesocial science that fuses plausible behavior with formalized logics matchedagainst empirical evidence, essential in showing how novel patterns ofurbanization such as desakota emerge
Dynamic coordinated control laws in multiple agent models
We present an active control scheme of a kinetic model of swarming. It has
been shown previously that the global control scheme for the model, presented
in \cite{JK04}, gives rise to spontaneous collective organization of agents
into a unified coherent swarm, via a long-range attractive and short-range
repulsive potential. We extend these results by presenting control laws whereby
a single swarm is broken into independently functioning subswarm clusters. The
transition between one coordinated swarm and multiple clustered subswarms is
managed simply with a homotopy parameter. Additionally, we present as an
alternate formulation, a local control law for the same model, which implements
dynamic barrier avoidance behavior, and in which swarm coherence emerges
spontaneously.Comment: 20 pages, 6 figure
Cellular Automata
Modelling and simulation are disciplines of major importance for science and engineering. There is no science without models, and simulation has nowadays become a very useful tool, sometimes unavoidable, for development of both science and engineering. The main attractive feature of cellular automata is that, in spite of their conceptual simplicity which allows an easiness of implementation for computer simulation, as a detailed and complete mathematical analysis in principle, they are able to exhibit a wide variety of amazingly complex behaviour. This feature of cellular automata has attracted the researchers' attention from a wide variety of divergent fields of the exact disciplines of science and engineering, but also of the social sciences, and sometimes beyond. The collective complex behaviour of numerous systems, which emerge from the interaction of a multitude of simple individuals, is being conveniently modelled and simulated with cellular automata for very different purposes. In this book, a number of innovative applications of cellular automata models in the fields of Quantum Computing, Materials Science, Cryptography and Coding, and Robotics and Image Processing are presented
Data mining as a tool for environmental scientists
Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous
Multi-Agent Fitness Functions For Evolutionary Architecture
The dynamics of crowd movements are self-organising and often involve complex pattern formations.
Although computational models have recently been developed, it is unclear how
well their underlying methods capture local dynamics and longer-range aspects, such as evacuation.
A major part of this thesis is devoted to an investigation of current methods, and
where required, the development of alternatives. The main purpose is to utilise realistic models
of pedestrian crowds in the design of fitness functions for an evolutionary approach to
architectural design.
We critically review the state-of-the-art in pedestrian and evacuation dynamics. The concept
of 'Multi-Agent System' embraces a number of approaches, which together encompass
important local and longer-range aspects. Early investigations focus on methods-cellular
automata and attractor fields-designed to capture these respective levels.
The assumption that pattern formations in crowds result from local processes is reflected in
two dimensional cellular automata models, where mathematical rules operate in local neighbourhoods.
We investigate an established cellular automata and show that lane-formation
patterns are stable only in a low-valued density range. Above this range, such patterns suddenly
randomise. By identifying and then constraining the source of this randomness, we
are only able to achieve a small degree of improvement. Moreover, when we try to integrate
the model with attractor fields, no useful behaviour is achieved, and much of the randomness
persists. Investigations indicate that the unwanted randomness is associated with 2-lattice
phase transitions, where local dynamics get invaded by giant-component clusters during the
onset of lattice percolation. Through this in-depth investigation, the general limits to cellular
automata are ascertained-these methods are not designed with lattice percolation properties
in mind and resulting models depend, often critically, on arbitrarily chosen neighbourhoods.
We embark on the development of new and more flexible methodologies. Rather than
treating local and global dynamics as separate entities, we combine them. Our methods
are responsive to percolation, and are designed around the following principles: 1) Inclusive
search provides an optimal path between a pedestrian origin and destination. 2) Dynamic
boundaries protect search and are based on percolation probabilities, calculated from local
density regimes. In this way, more robust dynamics are achieved. Simultaneously, longer-range
behaviours are also specified. 3) Network-level dynamics further relax the constraints
of lattice percolation and allow a wider range of pedestrian interactions.
Having defined our methods, we demonstrate their usefulness by applying them to lane-formation
and evacuation scenarios. Results reproduce the general patterns found in real
crowds.
We then turn to evolution. This preliminary work is intended to motivate future research in
the field of Evolutionary Architecture. We develop a genotype-phenotype mapping, which produces
complex architectures, and demonstrate the use of a crowd-flow model in a phenotype-fitness
mapping. We discuss results from evolutionary simulations, which suggest that obstacles
may have some beneficial effect on crowd evacuation. We conclude with a summary,
discussion of methodological limitations, and suggestions for future research
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