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Complexity is worth studying as a subject for its own sake. We intend to investigate complexity in the context of a fairly broad class of dynamical systems (known as sequential machines). A sequential machine is a perfectly good model of an organization (or organism) which strives for survival, acting under resource and time constraints. For a given complexity level of the system design we could find the level of contact indicating—roughly—the level of understanding of the system transformation. We call this the control complexity. The “information technology” is generated by the lattice of partitions of the state space of the system realized by a serial‐parallel decomposition into component systems.

Studies the concept of the Cartesian composition of fuzzy finite state machines. Shows that fuzzy finite state machines and their Cartesian composition share many structural properties. Some of these properties are singly generated; retrievability, connectedness, strong connectedness, commutativity, perfection and state independence. Thus a fuzzy finite state machine which is a Cartesian composition of submachines can be studied in terms of smaller machines.

Presents fundamental results on fuzzy Mealy machines. Unlike the classical Mealy machine which requires two functions, one to describe the next state and another to describe the output, a fuzzy Mealy machine requires only one fuzzy function to characterize completely the next state and the output produced. Apart from the obvious generalization that can be obtained from corresponding results on fuzzy finite state machines by introduction of an output associated with each transition, introduces the concept of an interval partition of [0, 1] and uses it to obtain more general results.

The historical perspective of the emergence of cybernetics as the science of control, communication and computation in machines and living tissues, and integrated and complex man‐machine systems as a unitary discipline of General Systems Theory is presented. In the second section both the theories of cybernetics and general systems are shown to be the same in motivation, requirements, characteristics, attributes and behaviour. In the third section cybernetic systems such as the class of finite state machines are established as a subclass of general dynamical systems. In the last section the cybernetics as a general and universal theory of action, including social praxis in general comprehending technical and moral praxis, is presented.

The purpose of this study is to develop a Turing machine or a finite automaton, which scans the input data tape in the form of DNA sequences and inspires the basic design of a DNA computer.

This model based on a splicing system can solve fuzzy reasoning autonomously by using DNA sequences and human assisted protocols. Its hardware consists of class IIS restriction enzyme and T4 DNA ligase while the software consists of double stranded DNA sequences and transition molecules which are capable of encoding fuzzy rules. Upon mixing solutions containing these components, the automaton undergoes a cascade of cleaving and splicing cycles to produce the computational result in form of double stranded DNA sequence representing automaton's final state.

In this work, the authors have fused the idea of a splicing system with the automata theory to develop fuzzy molecular automaton in which 1,018 processors can work in parallel, requiring a trillion times less space for information storage, is 105 times faster than the existing super computer and 1,019 power operations can be performed using one Joule of energy.

This paper presents a generalized model for biologically inspired computation in nano scale.

The purpose of this paper is to provide a method for the generation of information machines for part mating process diagnosis. Recognition of contact states between parts during robotized part mating represents a significant element of the system for active compliant robot motion. All proposed information machines for contact states recognition will recognize one of the possible contact states even when irregular events in the process occur, and the active motion planner will continue to send commands to robot controller according to the planned trajectory.

The presented framework is based on the general theory of automata and formal languages. Starting from possible regular contact states transitions in part mating, the authors create an automaton for diagnostics, which, besides regular, accepts all irregular (observable and unobservable) process sequences.

Contact states do not appear arbitrarily during regular processes, but in certain context. Theory of automata represents a solid basis for contextual recognition and diagnosis of irregularities in part mating.

The proposed methodology is elaborated and experimentally verified using an example of cylindrical part mating, and stick-slip effect as an observable irregularity. The future work will address the generation of diagnosers for other types of part mating tasks and extension of the set of observable irregularities.

The process diagnosis increases the robustness of active compliant motion system.

Although very important feedback information provider for active motion planner, part mating process monitoring was not frequently addressed in the past. In this paper, the authors propose a methodology for generation of part mating process diagnoser that is based on general automata theory.

The aim is identifying and analyzing some well‐defined types of emergence where the paper uses ideas from machine learning and artificial intelligence to provide the model of cellular automata based on rough set theory and response in simulated cars.

This paper proposes, as practical part, a road traffic system based on two‐dimensional cellular automata combined with rough set theory to model the flow and jamming that is suitable to an urban environment.

The automaton mimics realistic traffic rules that apply in everyday experience.

The modeled development process in this paper involves simulated processes of evolution, learning and self‐organization.

Recently, the examination and modeling of vehicular traffic has become an important subject of research.

The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of the state machine behavior, which can give an emergent to the model.

In evolutionary robotics (ER), robotic control systems are subject to a developmental process inspired by natural evolution. The purpose of this paper is to utilize a control system representation based on finite state machines (FSMs) to build a decentralized online‐evolutionary framework for swarms of mobile robots.

A new recombination operator for multi‐parental generation of offspring is presented and a known mutation operator is extended to harden parts of genotypes involved in good behavior, thus narrowing down the dimensions of the search space. A storage called memory genome for archiving the best genomes of every robot introduces a decentralized elitist strategy. These operators are studied in a factorial set of experiments by evolving two different benchmark behaviors such as collision avoidance and gate passing on a simulated swarm of robots. A comparison with a related approach is provided.

The framework is capable of robustly evolving the benchmark behaviors. The memory genome and the number of parents for reproduction highly influence the quality of the results; the recombination operator leads to an improvement in certain parameter combinations only.

Future studies should focus on further improving mutation and recombination. Generality statements should be made by studying more behaviors and there is a need for experimental studies with real robots.

The design of decentralized ER frameworks is improved.

The framework is robust and has the advantage that the resulting controllers are easier to analyze than in approaches based on artificial neural networks. The findings suggest improvements in the general design of decentralized ER frameworks.

The purpose of this research paper is to develop an algorithm/methodology for the reduction of a fuzzy recognizer through an algebraic concept such as homomorphism.

The approach of this research is to introduce the concepts, compatible with the purpose of this paper, and then to find a necessary and sufficient condition for the reduction of a fuzzy recognizer.

A fuzzy Σ‐recognizer M with non‐empty fuzzy initial state and the behavior A is reduced if and only if it is isomorphic to M^A.

The research proposes an algebraic method for the reduction of a fuzzy recognizer. The problem of finding dispensable fuzzy productions of a regular fuzzy grammar may be tackled by the use this algebraic method, as fuzzy recognizers and fuzzy regular grammars are equivalent.

The concepts and the methodology are original. The work is useful to the researchers in the field of fuzzy automata, fuzzy grammars and pattern recognition.

In this study, human activity with finite and specific ranking is modeled with finite state machine, and an application for human–robot interaction was realized. A robot arm was designed that makes specific movements. The purpose of this paper is to create a language associated to a complex task, which was then used to teach individuals by the robot that knows the language.

Although the complex task is known by the robot, it is not known by the human. When the application is started, the robot continuously checks the specific task performed by the human. To carry out the control, the human hand is tracked. For this, the image processing techniques and the particle filter (PF) based on the Bayesian tracking method are used. To determine the complex task performed by the human, the task is divided into a series of sub-tasks. To identify the sequence of the sub-tasks, a push-down automata that uses a context-free grammar language structure is developed. Depending on the correctness of the sequence of the sub-tasks performed by humans, the robot produces different outputs.

This application was carried out for 15 individuals. In total, 11 out of the 15 individuals completed the complex task correctly by following the different outputs.

This type of study is suitable for applications to improve human intelligence and to enable people to learn quickly. Also, the risky tasks of a person working in a production or assembly line can be controlled with such applications by the robots.