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 stop-and-go 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 self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well. CONTENTS

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...

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.

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 agent-based system. We illustrate our approach by applying it to analyze several agent-based systems, including coalition formation in an electronic marketplace, and foraging and collaboration in a group of robots. 1.

In 1981, Richard Feynman gave a talk at a conference hosted by the MIT Information Mechanics Group. This talk was entitled "Simulating Physics with Computers," and is reproduced in this volume. In this talk Feynman asked whether it is possible that, at some extremely microscopic scale, nature may operate exactly like discrete computer-logic. In particular, he discussed whether crystalline arrays of logic called Cellular Automata (CA) might be able to simulate our known laws of physics in a direct fashion. This question had been the subject of long and heated debates between him and his good friend Edward Fredkin (the head of the MIT Group) who has long maintained that some sort of discrete classicalinformation model will eventually replace continuous different

. We introduce a class of embedded-particle 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 embedded-particle 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...

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.

Wireless ad hoc networks of sensor nodes are envisioned to be deployed in the physical environment to monitor a wide variety of real-world 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 user-defined 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 application-specific 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.

Previous computational models of self-replication using cellular automata have been manually designed, a difficult and time-consuming process. We show here how genetic algorithms can be applied to automatically discover rules governing self-replicating structures. The main difficulty in this problem lies in the choice of the fitness evaluation technique. The solution we present is based on a multiobjective fitness function consisting of three independent measures: growth in number of components, relative positioning of components, and the multiplicity of replicants. We introduce a new paradigm for cellular automata models with weak rotational symmetry, called orientation insensitive input, and hypothesize that it facilitates discovery of self-replicating structures by reducing search-space sizes. Experimental yields of self-replicating structures discovered using our technique are shown to be statistically significant. The discovered self-replicating structures compare favorably in terms of simplicity with those generated manually in the past, but differ in unexpected ways. These results suggest that further exploration in the space of possible self-replicating structures will yield additional new structures. Furthermore, this research sheds light on the process of creating self-replicating structures, opening the door to future studies on the discovery of novel self-replicating molecules and self-replicating assemblers in nanotechnology.

We investigate a soliton cellular automaton (Box-Ball 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