Continuous automaton

About: Continuous automaton is a research topic. Over the lifetime, 947 publications have been published within this topic receiving 17417 citations.

Optimal amnesic probabilistic automata or how to learn and classify proteins in linear time and space

Abstract: Statistical modeling of sequences is a central paradigm of machine learning that finds multiple uses in computational molecular biology and many other domains. The probabilistic automata typically built in these contexts are subtended by uniform, fixed-memory Markov models. In practice, such automata tend to be unnecessarily bulky and computationally imposing both during their synthesis and use. In [8], much more compact, tree-shaped variants of probabilistic automata are built which assume an underlying Markov process of variable memory length. In [3, 4], these variants, called Probabilistic Suffix Trees (PSTs) were successfully applied to learning and prediction of protein families. The process of learning the automaton from a given training set S of sequences requires Θ (Ln2) worst-case time, where n is the total length of the sequences in S and L is the length of a longest substring of S to be considered for a candidate state in the automaton. Once the automaton is built, predicting the likelihood of a query sequence of m characters may cost time Θ (m2) in the worst case.The main contribution of this paper is to introduce automata equivalent to PSTs but having the following properties: learning the automaton takes O (n) time.prediction of a string of m symbols by the automaton takes O (m) time.Along the way, the paper presents an evolving learning sheme, and addresses notions of empirical probability and related efficient computation,possibly a by-product of more general interest.

Evidence for a new phase in the Domany-Kinzel cellular automaton

Abstract: We consider a generalized version (including anisotropy) of the stochastic one-dimensional cellular automaton studied by Domany and Kinzel. It recovers Wolfram-like deterministic cellular automata as particular cases. The phase diagram presents three (and not two, as previously suggested) phases which were detected through the numerical study of both the order parameter and the sensitivity to initial conditions. The various universality classes are exhibited as well.

Cellular automaton generating topological structures

Abstract: This paper describes a cellular automaton based on adaptive function of living systems. We simulated the behavior on a computer giving each cell local rules such as 'death', 'birth', and 'division' like an organism. The computational results showed that the model generates a clear framed structure for a mechanical condition. We also reported on diverse topological structures inhered in the system.

Controlling Self-Reconfiguration using Cellular Automata and Gradients

Abstract: Self-reconfigurable robots are built from modules, which are autonomously able to change the way they are connected. Such a robot can, through this self-reconfiguration process, change its shape. The process has proven to be difficult to control, because it involves control of a distributed system of mechanically coupled modules connected in time-varying ways. In this paper we present an approach to the self-reconfiguration problem where the desired configuration is grown from an initial seed module. Seeds produce growth by creating a gradient in the system, using local communication, which spare modules climb to locate the seed. The growth is guided by a cellular automaton, which is automatically generated based on a three-dimensional CAD model or a mathematical description of the desired configuration. The approach is evaluated in simulation and we find that the self-reconfiguration process always converges and the time to complete a configuration scales approximately linearly with the number of modules. However, an open question is how the simulation results transfer to a physically realized self-reconfigurable robot.