CA-NEAT: Evolved Compositional Pattern Producing Networks for Cellular Automata Morphogenesis and Replication

Cellular Automata (CA) are a remarkable example of morphogenetic system, where cells grow and self-organise through local interactions. CA have been used as abstractions of biological development and artificial life. Such systems have been able to show properties that are often desirable but difficult to achieve in engineered systems, e.g. morphogenesis and replication of regular patterns without any form of centralized coordination. However, cellular systems are hard to program (i.e. evolve) and control, especially when the number of cell states and neighbourhood increase. In this paper, we propose a new principle of morphogenesis based on Compositional Pattern Producing Networks (CPPNs), an abstraction of development that has been able to produce complex structural motifs without local interactions. CPPNs are used as Cellular Automata genotypes and evolved with a NeuroEvolution of Augmenting Topologies (NEAT) algorithm. This allows complexification of genomes throughout evolution with phenotypes emerging from self-organisation through development based on local interactions. In this paper, the problems of 2D pattern morphogenesis and replication are investigated. Results show that CA-NEAT is an appropriate means of approaching cellular systems engineering, especially for future applications where natural levels of complexity are targeted. We argue that CA-NEAT could provide a valuable mapping for morphogenetic systems, beyond cellular automata systems, where development through local interactions is desired

A review of Multi-Agent Simulation Models in Agriculture

Multi-Agent Simulation (MAS) models are intended to capture emergent properties of complex systems that are not amenable to equilibrium analysis. They are beginning to see some use for analysing agricultural systems. The paper reports on work in progress to create a MAS for specific sectors in New Zealand agriculture. One part of the paper focuses on options for modelling land and other resources such as water, labour and capital in this model, as well as markets for exchanging resources and commodities. A second part considers options for modelling agent heterogeneity, especially risk preferences of farmers, and the impacts on decision-making. The final section outlines the MAS that the authors will be constructing over the next few years and the types of research questions that the model will help investigate.multi-agent simulation models, modelling, agent-based model, cellular automata, decision-making, Crop Production/Industries, Environmental Economics and Policy, Farm Management, Land Economics/Use, Livestock Production/Industries,

Multiscale Modeling of Toxoplasma gondii

Toxoplasma gondii is a potentially deadly parasite that uses a very unique way of manipulating the cell and immune systems. To investigate the mechanics of how the parasite spreads within hosts, several interwoven topics related to the study of within-host dynamics of Toxoplasma gondii are presented here. Understanding the complicated methods of how the parasite grows, dies, invades, replicates, and evades the host immune response is the critical aim of this independent research. Understanding the processes of acute and chronic infection are studied independently, followed by modeling the two processes in the same model. Finally, the dynamic models are simulated within a 3D mesh representation of a mouse brain to visualize the infection spreading during the acute and chronic phase. The results presented shed light onto the effects of the immune response, cyst volume growth, and the dependence of multiple stages in the dissemination of the parasite within a host

Self-Replicating Structures in a Cellular Automata Space

Biological experience and intuition suggest that self-replication is an inherently complex phenomenon, and early cellular automata self-replication models developed by computer scientists and mathematicians supported that view. However, since von~Neumann's original work in the 1950's, the study of cellular automata models of self-replicating systems has progressively led to smaller and simpler systems. This thesis demonstrates for the first time that it is possible to create automatically self-replicating structures in cellular automata models rather than, as has been done in the past, to design them manually. These emergent self-replicating structures employ a General Purpose Self-Replicating cellular automata rule set which can support the replication of structures of different sizes and their growth from smaller to larger ones. This thesis also demonstrates that, by letting self-replicating structures carry additional information besides replication instructions, they can be used to solve computationally hard problems such as the Satisfiability (SAT) problem. It is shown that self-replicating structures can be made to carry characteristic codes and selection forces can be implemented in cellular automata space. This study opens the door to further studies that could lead to general, solution-evolvable structures and truly self-programming systems. (Also cross-referenced as UMIACS-TR-96-85

A Model of the Rise and Fall of Roads

Transportation network planning decisions made at one point of time can have profound impacts in the future. However, transportation networks are usually assumed tobe static in models of land use. A better understanding of the natural growth pattern of roads will provide valuable guidance to planners who try to shape the future network. This paper analyzes the relationships between network supply and travel demand, and describes a road development and degeneration mechanism microscopically at the linklevel. A simulation model of transportation network dynamics is developed, involving iterative evolution of travel demand patterns, network revenue policies, cost estimation,and investment rules. The model is applied to a real-world congesting network – the Twin Cities transportation network which comprises nearly 8,000 nodes and more than 20,000 links, using network data collected since year 1978. Four experiments are carried out with different initial conditions and constraints, the results from which allow us toexplore model properties such as computational feasibility, qualitative implications, potential calibration procedures, and predictive value. The hypothesis that roadhierarchies are emergent properties of transportation networks is confirmed, and the underlying reasons discovered. Spatial distribution of capacity, traffic flow, andcongestion in the transportation network is tracked over time. Potential improvements to the model in particular and future research directions in transportation network dynamicsin general are also discussed.Transportation network dynamics, Urban planning, Road suppl

Searching for patterns in Conway's Game of Life

Conway’s Game of Life (Life) is a simple cellular automaton, discovered by John Conway in 1970, that exhibits complex emergent behavior. Life-enthusiasts have been looking for building blocks with specific properties (patterns) to answer unsolved problems in Life for the past five decades. Finding patterns in Life is difficult due to the large search space. Current search algorithms use an explorative approach based on the rules of the game, but this can only sample a small fraction of the search space. More recently, people have used Sat solvers to search for patterns. These solvers are not specifically tuned to this problem and thus waste a lot of time processing Life’s rules in an engine that does not understand them. We propose a novel Sat-based approach that replaces the binary tree used by traditional Sat solvers with a grid-based approach, complemented by an injection of Game of Life specific knowledge. This leads to a significant speedup in searching. As a fortunate side effect, our solver can be generalized to solve general Sat problems. Because it is grid-based, all manipulations are embarrassingly parallel, allowing implementation on massively parallel hardware

Emergent simulation of cell-like shapes satisfying the conditions of life using lattice-type multiset chemical model

It is one of the great challenges of science to clarify when, where, why, and how the first life arose, and what the first life was. Here, assumed the conditions of life are 1) bounded, 2) replicating, 3) able to inherit information, and 4) metabolizing energy. The various hypotheses also provide little explanation of how the four conditions for life were established. It is not always clear how a chemical process that simultaneously satisfies all four conditions emerged after the materials for life were in place. This study considered a model (Multi-set chemical lattice model) that allows virtual molecules of multiple types to be placed in each cell on a two-dimensional space. Using only the processes of molecular diffusion, reaction, and polymerization, by modeling chemical reactions by 15 kinds of molecules and two kinds of polymerized molecules, and using the morphogenesis rule of the Turing model, it was able to model and show the process of the emergence of cell-like form having the four conditions of life. This model will allow us to revisit and refine each of the hypotheses of the birth of life in the future