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106
Traffic and related selfdriven manyparticle systems
, 2000
"... Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ‘‘phantom traffic jams’ ’ even though drivers all like to drive fast? ..."
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Cited by 146 (23 self)
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Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ‘‘phantom traffic jams’ ’ even though drivers all like to drive fast? What are the mechanisms behind stopandgo traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ‘‘freeze by heating’’? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to selfdriven manyparticle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particlebased), mesoscopic (gaskinetic), and macroscopic (fluiddynamic) models. Attention is also paid to the formulation of a micromacro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for selfdriven manyparticle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socioeconomic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well.
Hierarchical Learning with Procedural Abstraction Mechanisms
, 1997
"... Evolutionary computation (EC) consists of the design and analysis of probabilistic algorithms inspired by the principles of natural selection and variation. Genetic Programming (GP) is one subfield of EC that emphasizes desirable features such as the use of procedural representations, the capability ..."
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Cited by 33 (2 self)
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Evolutionary computation (EC) consists of the design and analysis of probabilistic algorithms inspired by the principles of natural selection and variation. Genetic Programming (GP) is one subfield of EC that emphasizes desirable features such as the use of procedural representations, the capability to discover and exploit intrinsic characteristics of the application domain, and the flexibility to adapt the shape and complexity of learned models. Approaches that learn monolithic representations are considerably less likely to be effective for complex problems, and standard GP is no exception. The main goal of this dissertation is to extend GP capabilities with automatic mechanisms to cope with problems of increasing complexity. Humans succeed here by skillfully using hierarchical decomposition and abstraction mechanisms. The translation of such mechanisms into a general computer implementation is a tremendous challenge, which requires a firm understanding of the interplay between repr...
Algorithms for Generic Role Assignment in Wireless Sensor Networks
 in SenSys ’05: Proceedings of the 3rd international conference on Embedded networked sensor systems
, 2005
"... We consider configuration of wireless sensor networks, where certain functions must be automatically assigned to sensor nodes, such that the properties of a sensor node (e.g., remaining energy, network neighbors) match the requirements of the assigned function. Essentially, sensor nodes take on cert ..."
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Cited by 33 (3 self)
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We consider configuration of wireless sensor networks, where certain functions must be automatically assigned to sensor nodes, such that the properties of a sensor node (e.g., remaining energy, network neighbors) match the requirements of the assigned function. Essentially, sensor nodes take on certain roles in the network as a result of configuration. To help developers with such configuration tasks for a variety of applications, we propose generic role assignment as a programming abstraction, where roles and rules for their assignment can be easily specified using a configuration language. We present such a role specification language and distributed algorithms for role assignment according to such specifications. We evaluate our approach and show that efficient and robust generic role assignment is practically feasible for wireless sensor networks.
A General Methodology for Mathematical Analysis of MultiAgent Systems
 USC Information Sciences
, 2001
"... We propose a general mathematical methodology for studying the dynamics of multiagent systems in which complex collective behavior arises out of local interactions between many simple agents. The mathematical model is composed of a system of coupled differential equations describing the macroscop ..."
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Cited by 28 (3 self)
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We propose a general mathematical methodology for studying the dynamics of multiagent systems in which complex collective behavior arises out of local interactions between many simple agents. The mathematical model is composed of a system of coupled differential equations describing the macroscopic, or collective, dynamics of an agentbased system. We illustrate our approach by applying it to analyze several agentbased systems, including coalition formation in an electronic marketplace, and foraging and collaboration in a group of robots. 1.
CRYSTALLINE COMPUTATION
 CHAPTER 18 OF FEYNMAN AND COMPUTATION (A. HEY, ED.)
, 1999
"... Discrete lattice systems have had a long and productive history in physics. Examples range from exact theoretical models studied in statistical mechanics to approximate numerical treatments of continuum models. There has, however, been relatively little attention paid to exact lattice models which o ..."
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Cited by 28 (7 self)
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Discrete lattice systems have had a long and productive history in physics. Examples range from exact theoretical models studied in statistical mechanics to approximate numerical treatments of continuum models. There has, however, been relatively little attention paid to exact lattice models which obey an invertible dynamics: from any state of the dynamical system you can infer the previous state. This kind of microscopic reversibility is an important property of all microscopic physical dynamics. Invertible lattice systems become even more physically realistic if we impose locality of interaction and exact conservation laws. In fact, some invertible and momentum conserving lattice dynamics—in which discrete particles hop between neighboring lattice sites at discrete times—accurately reproduce hydrodynamics in the macroscopic limit. These kinds of discrete systems not only provide an intriguing informationdynamics approach to modeling macroscopic physics, but they may also be supremely practical. Exactly the same properties that make these models physically realistic also make them efficiently realizable. Algorithms that incorporate constraints such as locality of interaction and invertibility can be run on microscopic physical hardware that shares these constraints. Such hardware can, in principle, achieve a higher density and rate of computation than any other kind of computer. Thus it is interesting to construct discrete lattice dynamics which are more physicslike both in order to capture more of the richness of physical dynamics in informational models, and in order to improve our ability to harness physics for computation. In this chapter, we discuss techniques for bringing discrete lattice dynamics closer to physics, and some of the interesting consequences of doing so.
The emergence of hierarchy in transportation networks
 Annals of Regional Science
, 2005
"... A transportation network is a complex system that exhibits the properties of selforganization and emergence. Previous research in dynamics related to transportation networks focuses on traffic assignment or traffic management. This research concentrates on the dynamics of the orientation of major ro ..."
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Cited by 25 (16 self)
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A transportation network is a complex system that exhibits the properties of selforganization and emergence. Previous research in dynamics related to transportation networks focuses on traffic assignment or traffic management. This research concentrates on the dynamics of the orientation of major roads in a network and abstractly models these dynamics to understand the basic properties of transportation networks. A model is developed to capture the dynamics that leads to a hierarchical arrangement of roads for a given network structure and land use distribution. Localized investment rules – revenue produced by traffic on a link is invested for that link’s own development – are employed. Under reasonable parameters, these investment rules, coupled with traveler behavior, and underlying network topology result in the emergence of a hierarchical pattern. Hypothetical networks subject to certain conditions are tested with this model to explore the network properties. Though hierarchies seem to be designed by planners and engineers, the results show that they are intrinsic properties of networks. Also, the results show that roads, specific routes with continuous attributes, are emergent properties of transportation networks.
Mechanisms of Emergent Computation in Cellular Automata
 Parallel Problem Solving from Nature Proceedings Vth Workshop PPSN V
, 1998
"... . We introduce a class of embeddedparticle models for describing the emergent computational strategies observed in cellular automata (CAs) that were evolved for performing certain computational tasks. The models are evaluated by comparing their estimated performances with the actual performances of ..."
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Cited by 25 (7 self)
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. We introduce a class of embeddedparticle models for describing the emergent computational strategies observed in cellular automata (CAs) that were evolved for performing certain computational tasks. The models are evaluated by comparing their estimated performances with the actual performances of the CAs they model. The results show, via a close quantitative agreement, that the embeddedparticle framework captures the main information processing mechanisms of the emergent computation that arise in these evolved CAs. 1 Introduction In previous work we have used genetic algorithms (GAs) to evolve cellular automata (CAs) to perform computational tasks that require global coordination. The evolving cellular automata framework has provided a direct approach to studying how evolution (natural or artificial) can create dynamical systems that perform emergent computation; that is, how it can find dynamical systems in which the interaction of simple components with local information storage...
Generic Role Assignment for Wireless Sensor Networks
 In Proceedings of the 11th ACM SIGOPS European Workshop
, 2004
"... Wireless ad hoc networks of sensor nodes are envisioned to be deployed in the physical environment to monitor a wide variety of realworld phenomena. Almost any sensor network application requires some form of selfconfiguration, where sensor nodes take on specific functions or roles in the network w ..."
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Cited by 24 (7 self)
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Wireless ad hoc networks of sensor nodes are envisioned to be deployed in the physical environment to monitor a wide variety of realworld phenomena. Almost any sensor network application requires some form of selfconfiguration, where sensor nodes take on specific functions or roles in the network without manual intervention. These roles may be based on varying sensor node properties (e.g., available sensors, location, network neighbors) and may be used to support applications requiring heterogeneous node functionality (e.g., clustering, data aggregation). In this paper we argue that the assignment of userdefined roles is a fundamental part of a wide range of sensor network applications. Consequently, a framework for assignment of roles to sensor nodes in an applicationspecific manner could significantly ease sensor network programming. We outline the general structure of such a framework and present a first approach to its realization. We demonstrate its utility and feasibility using a number of concrete examples.
Cellular Automata and Lattice Boltzmann Techniques: An Approach to Model and Simulate Complex Systems
 ADVANCES IN PHYSICS, SUBMITTED
, 1998
"... We discuss the cellular automata approach and its extensions, the lattice Boltzmann and multiparticle methods. The potential of these techniques is demonstrated in the case of modeling complex systems. In particular, we consider applications taken from various fields of physics, such as reactiondi ..."
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Cited by 24 (3 self)
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We discuss the cellular automata approach and its extensions, the lattice Boltzmann and multiparticle methods. The potential of these techniques is demonstrated in the case of modeling complex systems. In particular, we consider applications taken from various fields of physics, such as reactiondiffusion systems, pattern formation phenomena, fluid flows, fracture processes and road traffic models.
Fundamental cycle of a periodic boxball system
 University of Tokyo, Komaba, Tokyo 1538902, Japan atsuo@gokutan.c.utokyo.ac.jp Reiho Sakamoto: Department of Physics, Graduate School of Science, University of Tokyo, Hongo, Tokyo 1130033, Japan reiho@monet.phys.s.utokyo.ac.jp
"... We investigate a soliton cellular automaton (BoxBall system) with periodic boundary conditions. Since the cellular automaton is a deterministic dynamical system that takes only a finite number of states, it will exhibit periodic motion. We determine its fundamental cycle for a given initial state. ..."
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Cited by 20 (1 self)
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We investigate a soliton cellular automaton (BoxBall system) with periodic boundary conditions. Since the cellular automaton is a deterministic dynamical system that takes only a finite number of states, it will exhibit periodic motion. We determine its fundamental cycle for a given initial state. 1