Interaction with the environment is a key factor affecting the development of plants and plant ecosystems. In this paper we introduce a modeling framework that makes it possible to simulate and visualize a wide range of interactions at the level of plant architecture. This framework extends the formalism of Lindenmayer systems with constructs needed to model bi-directional information exchange between plants and their environment. We illustrate the proposed framework with models and simulations that capture the development of tree branches limited by collisions, the colonizing growth of clonal plants competing for space in favorable areas, the interaction between roots competing for water in the soil, and the competition within and between trees for access to light. Computer animation and visualization techniques make it possible to better understand the modeled processes and lead to realistic images of plants within their environmental context. CR categories: F.4.2 [Mathematical Logi...

A major impediment of cellular automata (CA) stems from the difficulty of utilizing their complex behavior to perform useful computations. Recent studies by [ Packard, 1988, Mitchell et al., 1994b ] have shown that CAs can be evolved to perform a computational task. In this paper non-uniform CAs are studied, where each cell may contain a different rule, in contrast to the original, uniform model. We describe experiments in which non-uniform CAs are evolved to perform the computational task using a local, co-evolutionary algorithm. For radius r = 3 we attain peak performance values of 0:92 comparable to those obtained for uniform CAs (0:93 \Gamma 0:95). This is notable considering the huge search spaces involved, much larger than the uniform case. Smaller radius CAs (previously unstudied in this context) attain performance values of 0:93 \Gamma 0:94. For r = 1 this is considerably higher than the maximal possible uniform CA performance of 0:83, suggesting that nonuniformity reduces con...

The aim of this paper is to introduce to the reader the main ideas of computing with membranes, a recent branch of (theoretical) molecular computing. In short, in a cell-like system, multisets of objects evolve according to given rules in the compartments defined by a membrane structure and compute natural numbers as the result of halting sequences of transitions. The model is parallel, nondeterministic. Many variants have already been considered and many problems about them were investigated. We present here some of these variants, focusing on two central classes of results: (1) characterizations of the recursively enumerable sets of numbers and (2) possibilities to solve NP-complete problems in polynomial — even linear — time (of course, by making use of an exponential space). The results are given without proofs. An almost complete bibliography of the domain, at the middle of October 2000, is

Self-reproducing, cellular automata-based systems developed to date broadly fall under two categories; the first consists of machines which are capable of performing elaborate tasks, yet are too complex to simulate, while the second consists of extremely simple machines which can be entirely implemented, yet lack any additional functionality aside from self-reproduction. In this paper we present a self-reproducing system which is completely realizable, while capable of executing any desired program, thereby exhibiting universal computation. Our starting point is a simple self-reproducing loop structure onto which we "attach" an executable program (Turing machine) along with its data. The three parts of our system (loop, program, data) are all reproduced, after which the program is run on the given data. The system reported in this paper has been simulated in its entirety; thus, we attain a viable, self-reproducing machine with programmable capabilities. 1 Introduction The study of art...

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 have previously shown that non-uniform cellular automata (CA) can be evolved to perform computational tasks, using the cellular programming algorithm. In this paper we focus on two novel issues, namely the evolution of asynchronous CAs, and the scalability of our evolved systems. We find that asynchrony presents a more difficult case for evolution though good CAs can still be attained. We describe an empirically-derived scaling procedure by which successful CAs of any size may be obtained from a particular evolved system. Our motivation for this study stems in part by our desire to attain realistic systems that are more amenable to implementation as `evolving ware', evolware. 1 Introduction Cellular automata (CA) are dynamical systems in which space and time are discrete. A cellular automaton consists of an array of cells, each of which can be in one of a finite number of possible states, updated synchronously in discrete time steps according to a local, identical interaction rule...

Recent studies have shown that non-uniform cellular automata (CA), where cellular rules need not necessarily be identical, can be co-evolved to perform computational tasks. This paper extends these studies by generalizing on a second aspect of CAs, namely their standard, homogeneous connectivity. We study non-standard architectures, where each cell has a small, identical number of connections, yet not necessarily from its most immediate neighboring cells. We show that such architectures are computationally more efficient than standard architectures in solving global tasks, and also provide the reasoning for this. It is shown that one can successfully evolve non-standard architectures through a two-level evolutionary process, in which the cellular rules evolve concomitantly with the cellular connections. Specifically, studying the global density task, we identify the average cellular distance as a prime architectural parameter determining cellular automata performance. We carry out a ...

. Cellular automata (CA) are dynamical systems in which space and time are discrete, where each cell obeys the same rule and has a finite number of states. In this paper we study non-uniform CA, i.e. with non-uniform local interaction rules. Our focal point is the issue of universal computation, which has been proven for uniform automata using complicated designs embedded in cellular space. The computation-universal system presented here is simpler than previous ones, and is embedded in the minimal possible two-dimensional cellular space, namely 2-state, 5-neighbor (which is insufficient for universal computation in the uniform model). The space studied is quasi-uniform, meaning that a small number of rules is used (our final design consists of just two rules which is minimal), distributed such that most of the grid contains one rule except for an infinitely small region which contains the others. We maintain that such automata provide us with a simple, general model for studying Artif...

Interaction with the environment is a key factor affecting the development of plants and plant ecosystems. In this paper we introduce a modeling framework that makes it possible to simulate and visualize a wide range of interactions at the level of plant architecture. This framework extends the formalism of Lindenmayer systems with constructs needed to model bi−directional information exchange between plants and their environment. We illustrate the proposed framework with models and simulations that capture the development of tree branches limited by collisions, the colonizing growth of clonal plants competing for space in favorable areas, the interaction between roots competing for water in the soil, and the competition within and between trees for access to light. Computer animation and visualization techniques make it possible to better understand the modeled processes and lead to realistic images of plants within their environmental context. Keywords: scientific visualization, realistic image synthesis, software design, L−system,

Biological organisms are the most familiar examples of self-replicating systems, and until the late 1940s, the only instances formally researched. At that time, mathematicians and scientists began studying artificial selfreplicating systems when it became desirable to gain a deeper understanding of how complex systems are able to form and evolve. Initial models consisted of abstract logical machines, or automata, embedded in cellular spaces. The large complexities seen in these early models agreed with the intuition that self-replication was an inherently complex process. Later, it was learned that much of the complexity was due to the imposition of artificial requirements. This paper traces developments from complex, early models of self-replicating systems in cellular spaces to recent, less complex models. As a survey of past models, this paper provides an overview of numerous self-replicating systems as well as some recent models that rely on emergent processes and artificial evolution. 1.