Automaton Operation Research Articles

Teaching concepts related to finite automata using ComVis

AbstractThis paper describes educational software for visualization and finite automata simulation. The system was developed in Java programming language, and its main purpose is learning process improvement. Using this software, finite automata can be defined in a graphical editor in the form of a state diagram, or by defining the transition function using the transition table. Upon defining an automaton, it is possible to run a visual simulation of the automaton operation for an arbitrary input string, whereby a textual description of the automaton simulation is also obtained. Furthermore, the system allows for conversion of regular expressions to deterministic finite automata and nondeterministic finite automata. The simulation of Thompson's construction algorithm is implemented, so that transformation of a regular expression to the corresponding NFA can be monitored in a step‐by‐step process. The quantitative analysis and evaluation of the effectiveness of the simulator are based on a survey of undergraduate students at two universities. The majority of students found the tool a useful means for better understanding and mastering of finite automata concepts.

Algorithms for computing the sets of stochastic matrices with predefined properties, based on automaton models

The formed sets of ergodic stochastic matrices are focused on solving the problem of classifying probabilistic automaton models by certain criteria/parameters of similarities or differences in the structures of ergodic stochastic matrices using the methods of applied multivariate mathematical statistics. The algorithms developed allow forming a variety of the sets of stochastic matrices through changing the transition and random entry function in a probabilistic automaton. Transition function of the autonomous probabilistic automaton allows using the probabilistic automaton operation algorithm proposed to form the sets of ergodic stochastic matrices that differ in their potencies and structures, based on implementing state set permutations with repetitions and changing the random input variable probability distributions. Defining various autonomous deterministic automaton output functions, we can use the algorithm developed to form the sets of ergodic stochastic matrices that differ in their potencies, with a specified limiting vector, based on implementing the permutations of a set of output letters with repetitions. We are also presenting the evaluations of the potencies of the sets of ergodic stochastic matrices with rational elements, represented by autonomous probabilistic automata under the given constraints.

Общий подход к решению задач на графах коллективом автоматов

We propose a general method to solve graph problems by a set of automata (computational agents) located in vertices of undirected ordered connected rooted graph and communicating by passing messages along graph edges. The automata are semi-robots, i.e., their internal memory size is sufficient to store values depending on the number of vertices and edges of the graph (n and m, correspondingly) but is too small to store the complete graph description. Section 2 presents classification of graph-based distributed computational models depending on internal memory size of vertex automaton, vertex automaton operation time, and edge capacity (the number of messages that are passing along an edge simultaneously). We choose for further use the model of maximum parallelism, having zero automaton operation time and unbounded edge capacity. It allows to obtain lower complexity bounds on distributed graph problems. Section 3 describes algorithm complexity assessment rules. Section 4 presents basic message processing procedures and message classification according to paths passed by them and methods of message processing by vertex automaton. We propose to construct algorithms solving graph problems on the base of the procedures considered, and present some examples in further sections. Sections 5-9 describe distributed algorithms for spanning tree construction, for task termination detection (based on detection of absence of messages used by the task), for shortest paths tree construction, for graph vertices enumeration, for collecting graph structure information in the root automaton memory (if it is unbounded). The algorithms proposed has linear time complexity in n and m, and use linear in n and m number of messages. Section 10 considers construction of maximum weight path in a weighted graph, which is the NP problem. We propose the algorithm that for the sake of using unbounded number of messages can solve this problem in linear time in n and m. The conclusion summarizes the paper and draws directions of further research: constructing algorithms for other graph problems and generalization of the approach to directed, non-deterministic and dynamic graphs.

EL MALESTAR EN LA RELACIÓN MÉDICO- PACIENTE

The possibility to recognize the madness that certain norms generate, such as the one that pretends to transform the medical action into an automaton's operation is stated around the uneasiness in the physician-patient relationship. Physicians may assume a different position if they are willing to keep in themselves the sight of their own ignorance about the inalienable particularity of each sick person, and of the individual history that shelters the symptoms. Because even though we can assert that the patient's rights have been respected, we know nothing about him. In order to recognise his patient's particularity, and for the patient to acknowledge himself in that particularity, the doctor has to face him as if he was facing a mystery.

A syntactic methodology for automatic diagnosis by analysis of continuous time measurements using hierarchical signal representations

In this paper, we present a methodology for automatic diagnosis of systems characterized by continuous signals. For each condition considered, the methodology requires the development of an alphabet of signal primitives, and a set of hierarchical fuzzy automatons (HFAs). Each alphabet is adaptively obtained by training an adaptive resonance theory (ART2) architecture with signal segments from a particular condition. Then, the original signal is transformed into a string of vectors of primitives, where each vector of primitives replaces a signal segment in the original signal. The string, in turn, is presented to the HFA characterizing that particular condition. Each set of HFA consists of a main automaton identifying the entire signal, and several sub-automata each identifying a particular significant structure in the signal. A transition in the main automaton occurs (i.e., the main automaton moves from one state to another) if the corresponding subautomaton recognizes a token where a token is a portion of the string of vectors of signal primitives with a significant structure. The fuzziness in automaton operation adds flexibility to the operation of the automaton, enabling the processing of imperfect input, allowing for toleration measurement noise and other ambiguities. The methodology is applied to the problem of automatic electrocardiogram diagnosis.

Proposed experiment to assess operation of quantum cellular automaton cells

We propose an experiment for the detection of quantum cellular automaton (QCA) operation in a cell made up of four silicon quantum dots. We show that correlated switching between the two pairs of dots forming the cell can be clearly detected from the locking of the Coulomb blockade current peaks through each pair. We have performed numerical simulations on the basis of capacitance values obtained from experimental results on cells fabricated with silicon-on-insulator technology. This approach is shown to allow detection of QCA switching without the need for additional charge detectors, although fabrication parameters are rather critical.

Experiments and stability in group automata

If one considers abstract deterministic automata as “black boxes”, information about the interior of the black box is available by identifier experiments (of first- or second-order). Effects by wrong automaton operation disappear under certain stability properties of the automaton (see [7]). In this note we give a bound for stability and study under which conditions identifier experiments exist if the state set of the automaton is a (not necessarily abelian) group with multiple operators.

A reorganization scheme for a hierarchical system of learning automata

Many problems in adaptive control, pattern recognition, filtering, identification, and artificial intelligence can be viewed as adaptive parameter optimization problems. The learning automaton approach to these problems has distinct advantages over the classic hillclimbing methods but suffers from high dimensionality. A hierarchical system of learning automata has been used to reduce this problem somewhat, but inefficiencies still remain, since no one hierarchical structure is optimal for the entire learning automaton operation. To resolve this problem, a reorganization scheme is introduced that uses inherit properties of ϵ-optimal learning automata to heuristically select hierarchical structures with minimal computational effort while maintaining equivalency. Simulation results demonstrate a significant reduction in convergence time when the reorganization scheme is used.