Requriements-Driven | Development for Agent Software
As Wolfram states in the legendary A Brand New Type Of Science, he observed similar-ant common convergences:
Despite the fact that each pattern differs at length, the amount of essentially various kinds of patterns is extremely limited. Indeed, it appears the patterns which arise can more often than not be assigned very easily to 1 of just four fundamental shapes:
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Cellular Automata (Stanford Encyclopedia of Philosophy)
Cellular automata (henceforth: CA) are discrete, abstract computational systems that have proved useful both as general models of complexity and as more specific representations of non-linear dynamics in a variety of scientific fields. Firstly, CA are (typically) spatially and temporally discrete: they are composed of a finite or denumerable set of homogenous, simple units, the atoms or cells. At each time unit, the cells instantiate one of a finite set of states. They evolve in parallel at discrete time steps, following state update functions or dynamical transition rules: the update of a cell state obtains by taking into account the states of cells in its local neighborhood (there are, therefore, no actions at a distance). Secondly, CA are abstract: they can be specified in purely mathematical terms and physical structures can implement them. Thirdly, CA are computational systems: they can compute functions and solve algorithmic problems. Despite functioning in a different way from traditional, Turing machine-like devices, CA with suitable rules can emulate a universal Turing machine (see entry), and therefore compute, given Turing’s thesis (see entry on Church-Turing thesis), anything computable.
This task regards the development of the first configuration. If you are using C++11, I believe the simplest way to keep the automaton involves vectors. By doing this, how big the automaton is adaptable. Because the stored data keeps a 2-dimensional form, it is advisable to keep automaton like a 2-dimensional vector (i.e. vectors inside a vector). With this particular setup, the automaton can be regarded as a grid. Each row from the grid is stored like a vector. Each row vector is within turn kept in the primary vector. Suppose you want to commence with a ten cell by 10 cell grid. The vector declaration would resemble the next:
This chapter reviews some fundamental concepts and outcomes of the idea of cellular automata (CA). Topics discussed include classical is a result of the 1960s, relations between various concepts of injectivity and surjectivity, and dynamical system concepts associated with chaos in CA. Most answers are reported without full proofs but may examples are supplied that illustrate the thought of an evidence. The classical results discussed range from the Garden-of-Eden theorem and also the Curtis–Hedlund–Lyndon theorem, along with the balance property of surjective CA. Different variants of sensitivity to initial conditions and mixing qualities are introduced and associated with one another. Also, algorithmic aspects and undecidability answers are pointed out.
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A cellular automaton model of wildfire propagation and extinction
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Understanding tumor invasion and metastasis is of crucial importance for both fundamental cancer research and clinical practice. In vitro experiments have established that the invasive growth of malignant tumors is characterized by the dendritic invasive branches composed of chains of tumor cells emanating from the primary tumor mass. The preponderance of previous tumor simulations focused on non-invasive (or proliferative) growth. The formation of the invasive cell chains and their interactions with the primary tumor mass and host microenvironment are not well understood. Here, we present a novel cellular automaton (CA) model that enables one to efficiently simulate invasive tumor growth in a heterogeneous host microenvironment. By taking into account a variety of microscopic-scale tumor-host interactions, including the short-range mechanical interactions between tumor cells and tumor stroma, degradation of the extracellular matrix by the invasive cells and oxygen/nutrient gradient driven cell motions, our CA model predicts a rich spectrum of growth dynamics and emergent behaviors of invasive tumors. Besides robustly reproducing the salient features of dendritic invasive growth, such as least-resistance paths of cells and intrabranch homotype attraction, we also predict nontrivial coupling between the growth dynamics of the primary tumor mass and the invasive cells. In addition, we show that the properties of the host microenvironment can significantly affect tumor morphology and growth dynamics, emphasizing the importance of understanding the tumor-host interaction. The capability of our CA model suggests that sophisticated in silico tools could eventually be utilized in clinical situations to predict neoplastic progression and propose individualized optimal treatment strategies.
Cellular automata (CA) are pretty straight forward mixers can simulate complex processes both in space and time. A CA includes six defining components: a framework, cells, an area, rules, initial conditions, as well as an update sequence. CA models are pretty straight forward, nominally deterministic yet able to showing phase changes and emergence, map easily to the data structures utilized in geographic computer, and are simple to implement and understand. It has led to their recognition for applications for example calculating land use changes and monitoring disease spread, among many more.
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