17,156 research outputs found
Computing Aggregate Properties of Preimages for 2D Cellular Automata
Computing properties of the set of precursors of a given configuration is a
common problem underlying many important questions about cellular automata.
Unfortunately, such computations quickly become intractable in dimension
greater than one. This paper presents an algorithm --- incremental aggregation
--- that can compute aggregate properties of the set of precursors
exponentially faster than na{\"i}ve approaches. The incremental aggregation
algorithm is demonstrated on two problems from the two-dimensional binary Game
of Life cellular automaton: precursor count distributions and higher-order mean
field theory coefficients. In both cases, incremental aggregation allows us to
obtain new results that were previously beyond reach
Cellular Automata Applications in Shortest Path Problem
Cellular Automata (CAs) are computational models that can capture the
essential features of systems in which global behavior emerges from the
collective effect of simple components, which interact locally. During the last
decades, CAs have been extensively used for mimicking several natural processes
and systems to find fine solutions in many complex hard to solve computer
science and engineering problems. Among them, the shortest path problem is one
of the most pronounced and highly studied problems that scientists have been
trying to tackle by using a plethora of methodologies and even unconventional
approaches. The proposed solutions are mainly justified by their ability to
provide a correct solution in a better time complexity than the renowned
Dijkstra's algorithm. Although there is a wide variety regarding the
algorithmic complexity of the algorithms suggested, spanning from simplistic
graph traversal algorithms to complex nature inspired and bio-mimicking
algorithms, in this chapter we focus on the successful application of CAs to
shortest path problem as found in various diverse disciplines like computer
science, swarm robotics, computer networks, decision science and biomimicking
of biological organisms' behaviour. In particular, an introduction on the first
CA-based algorithm tackling the shortest path problem is provided in detail.
After the short presentation of shortest path algorithms arriving from the
relaxization of the CAs principles, the application of the CA-based shortest
path definition on the coordinated motion of swarm robotics is also introduced.
Moreover, the CA based application of shortest path finding in computer
networks is presented in brief. Finally, a CA that models exactly the behavior
of a biological organism, namely the Physarum's behavior, finding the
minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From
software to wetware. Springer, 201
Computing by nowhere increasing complexity
A cellular automaton is presented whose governing rule is that the Kolmogorov
complexity of a cell's neighborhood may not increase when the cell's present
value is substituted for its future value. Using an approximation of this
two-dimensional Kolmogorov complexity the underlying automaton is shown to be
capable of simulating logic circuits. It is also shown to capture trianry logic
described by a quandle, a non-associative algebraic structure. A similar
automaton whose rule permits at times the increase of a cell's neighborhood
complexity is shown to produce animated entities which can be used as
information carriers akin to gliders in Conway's game of life
Non-Direct Encoding Method Based on Cellular Automata to Design Neural Network Architectures
Architecture design is a fundamental step in the successful application of Feed forward Neural Networks. In most cases a large number of neural networks architectures suitable to solve a problem exist and the architecture design is, unfortunately, still a human expert’s job. It depends heavily on the expert and on a tedious trial-and-error process. In the last years, many works have been focused on automatic resolution of the design of neural network architectures. Most of the methods are based on evolutionary computation paradigms. Some of the designed methods are based on direct representations of the parameters of the network. These representations do not allow scalability; thus, for representing large architectures very large structures are required. More interesting alternatives are represented by indirect schemes. They codify a compact representation of the neural network. In this work, an indirect constructive encoding scheme is proposed. This scheme is based on cellular automata representations and is inspired by the idea that only a few seeds for the initial configuration of a cellular automaton can produce a wide variety of feed forward neural networks architectures. The cellular approach is experimentally validated in different domains and compared with a direct codification scheme.Publicad
Finite automata for caching in matrix product algorithms
A diagram is introduced for visualizing matrix product states which makes
transparent a connection between matrix product factorizations of states and
operators, and complex weighted finite state automata. It is then shown how one
can proceed in the opposite direction: writing an automaton that ``generates''
an operator gives one an immediate matrix product factorization of it. Matrix
product factorizations have the advantage of reducing the cost of computing
expectation values by facilitating caching of intermediate calculations. Thus
our connection to complex weighted finite state automata yields insight into
what allows for efficient caching in matrix product algorithms. Finally, these
techniques are generalized to the case of multiple dimensions.Comment: 18 pages, 19 figures, LaTeX; numerous improvements have been made to
the manuscript in response to referee feedbac
Turing degrees of limit sets of cellular automata
Cellular automata are discrete dynamical systems and a model of computation.
The limit set of a cellular automaton consists of the configurations having an
infinite sequence of preimages. It is well known that these always contain a
computable point and that any non-trivial property on them is undecidable. We
go one step further in this article by giving a full characterization of the
sets of Turing degrees of cellular automata: they are the same as the sets of
Turing degrees of effectively closed sets containing a computable point
Parallel Implementations of Cellular Automata for Traffic Models
The Biham-Middleton-Levine (BML) traffic model is a simple two-dimensional,
discrete Cellular Automaton (CA) that has been used to study self-organization
and phase transitions arising in traffic flows. From the computational point of
view, the BML model exhibits the usual features of discrete CA, where the state
of the automaton are updated according to simple rules that depend on the state
of each cell and its neighbors. In this paper we study the impact of various
optimizations for speeding up CA computations by using the BML model as a case
study. In particular, we describe and analyze the impact of several parallel
implementations that rely on CPU features, such as multiple cores or SIMD
instructions, and on GPUs. Experimental evaluation provides quantitative
measures of the payoff of each technique in terms of speedup with respect to a
plain serial implementation. Our findings show that the performance gap between
CPU and GPU implementations of the BML traffic model can be reduced by clever
exploitation of all CPU features
A Formal Model For Real-Time Parallel Computation
The imposition of real-time constraints on a parallel computing environment-
specifically high-performance, cluster-computing systems- introduces a variety
of challenges with respect to the formal verification of the system's timing
properties. In this paper, we briefly motivate the need for such a system, and
we introduce an automaton-based method for performing such formal verification.
We define the concept of a consistent parallel timing system: a hybrid system
consisting of a set of timed automata (specifically, timed Buchi automata as
well as a timed variant of standard finite automata), intended to model the
timing properties of a well-behaved real-time parallel system. Finally, we give
a brief case study to demonstrate the concepts in the paper: a parallel matrix
multiplication kernel which operates within provable upper time bounds. We give
the algorithm used, a corresponding consistent parallel timing system, and
empirical results showing that the system operates under the specified timing
constraints.Comment: In Proceedings FTSCS 2012, arXiv:1212.657
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