Abstract not found

Combinatorial games lead to several interesting, clean problems in algorithms and complexity theory, many of which remain open. The purpose of this paper is to provide an overview of the area to encourage further research. In particular, we begin with general background in combinatorial game theory, which analyzes ideal play in perfect-information games. Then we survey results about the complexity of determining ideal play in these games, and the related problems of solving puzzles, in terms of both polynomial-time algorithms and computational intractability results. Our review of background and survey of algorithmic results are by no means complete, but should serve as a useful primer. 1

Modern unsteady (multi-)field visualizations require an effective reduction of the data to be displayed. From a huge amount of information the most informative parts have to be extracted. Instead of the fuzzy application dependent notion of feature, a new approach based on information theoretic concepts is introduced in this paper to detect important regions. This is accomplished by extending the concept of local statistical complexity from finite state cellular automata to discretized (multi-)fields. Thus, informative parts of the data can be highlighted in an application-independent, purely mathematical sense. The new measure can be applied to unsteady multifields on regular grids in any application domain. The ability to detect and visualize important parts is demonstrated using diffusion, flow, and weather simulations.

Abstract. We study herewith the simple threshold cellular automata (CA), as perhaps the simplest broad class of CA with non-additive (i.e., non-linear and non-affine) local update rules. We characterize all possible computations of the most interesting rule for such CA, namely, the Majority (MAJ) rule, both in the classical, parallel CA case, and in case of the corresponding sequential CA where the nodes update sequentially, one at a time. We compare and contrast the configuration spaces of arbitrary simple threshold automata in those two cases, and point out that some parallel threshold CA cannot be simulated by any of their sequential counterparts. We show that the temporal cycles exist only in case of (some) parallel simple threshold CA, but can never take place in sequential threshold CA. We also show that most threshold CA have very few fixed point configurations and few (if any) cycle configurations, and that, while the MAJ sequential and parallel CA may have many fixed points, nonetheless “almost all” configurations, in both parallel and sequential cases, are transient states. 1

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In this chapter, I review the main methods and techniques of complex systems science. As a

We introduce the outline of a multi-agent model that can be used for visualizing simulated user behavior to support the assessment of design performance. We will consider various performance indicators of building environments, which are related to user reaction to design decisions. This system may serve as a media tool in the design process for a better understanding of what the design will look like, especially for those cases where design or planning decisions will affect the behavior of individuals. The system is based on cellular automata and multi-agent simulation technology. The system simulates how agents move around in a particular 3D (or 2D) environment, in which space is represented as a lattice of cells. Agents represent objects or people with their own behavior, moving over the network. Each agent will be located in a simulated space, based on the cellular automata grid. Each iteration of the simulation is based on a parallel update of the agents conforming local rules. Ag...

Abstract—Location management is a very important and complex problem in mobile computing. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of location management scenarios. This paper investigates the use of cellular automata (CA) combined with genetic algorithms to create an evolving parallel reporting cells planning algorithm. In the reporting cell location management scheme, some cells in the network are designated as reporting cells; mobile terminals update their positions (location update) upon entering one of these reporting cells. To create such an evolving CA system, cells in the network are mapped to cellular units of the CA and neighborhoods for the CA is selected. GA is then used to discover efficient CA transition rules. The effectiveness of the GA and of the discovered CA rules is shown for a number of test problems. Index Terms—Cellular automata, genetic algorithms, mobile computing, mobility management. 1

The fire-spreading phenomenon is highly complex, and existing mathematical models of fire are so complex themselves that any possibility of analytical solution is precluded. Instead, there has been some success when studying fire spread by means of simulation. However, precise and reliable mathematical models are still under development.They require extensive computing resources, being adequate to run in batch mode but making it difficult to meet real-time deadlines.As fire scientists need to learn about the problem domain through experimentation, simulation software needs to be easily modified.The authors used different discrete event modeling techniques to deal with these problems. They have qualitatively compared the Discrete Event System Specification (DEVS) and Cell-DEVS simulation results against controlled laboratory experiments, which allowed them to validate both simulation models of fire spread. They were able to show how these techniques can improve the definition of fire models.

Contemporary artificial life (also known as “ALife”) is an interdisciplinary study of life and life-like processes. Its two most important qualities are that it focuses on the essential rather than the contingent features of living systems and that it attempts to understand living systems by artificially synthesizing extremely simple forms of them. These two qualities are connected. By synthesizing simple systems that are very life-like and yet very unfamiliar, artificial life constructively explores the boundaries of what is possible for life. At the moment, artificial life uses three different kinds of synthetic methods. “Soft ” artificial life creates computer simulations or other purely digital constructions that exhibit life-like behavior. “Hard” artificial life produces hardware implementations of life-like systems. “Wet ” artificial life involves the creation of life-like systems in a laboratory using biochemical materials. Contemporary artificial life is vigorous and diverse. So this chapter’s first goal is to convey what artificial life is like. It first briefly reviews the history of artificial life and illustrates the current research thrusts in contemporary “soft”, “hard”, and