Automaton

About: Automaton is a research topic. Over the lifetime, 2389 publications have been published within this topic receiving 53824 citations. The topic is also known as: automata & automated machine.

Learning Automata: Theory and Applications

Abstract: Preface. Contents. Notations. Introduction. Basic notations and definitions. Introduction. Contolled finite system. Control strategies. Dynamic characteristics of controlled finite systems and their structures. Adaptive strategies and learning automata. Classification of problems of adaptive control of finite systems. Reinforcement schemes for average loss function minimization. Introduction. Adaptive control and static systems and linear programming problem. Reinforcement schemes. Properties of reinforcement schemes Behaviour of learning automata for different Reinforcement Schemes. Introduction Reinforcement scheme of Narendra-Shapiro. Reinforcement scheme of Luce and Varshavskii-Verontsova. Bush-Mosteller reinforcement scheme. Projectional stochastic approximation algorithm. Conclusion. Multilevel systems for Automata. Introduction Hierarchical system .The connection between two level adaptive control and bilinear programming problem. Two-level hierarchical system of learning automata using a projectional stochastic approximation algorithm. Two level hierarchical system with transmission of current information to the lower level. Multilevel hierarchical learning system. Conclusion. Multimodal function optimization using learning automata. Introduction. Optimization using single learning automata. Optimization using a two level hierarchical system of learning automata. Conclusion. Application of Learning Automata. Introduction. Practical aspects. Multilevel learning control of a drying furnace. Hierarchical learning control of absorption column. Learning control of an evaporator. Adaptive choice of cyclic code in communications systems. Optimization of multimodal functions ( without constraints). Optimization in presence of constraints. Application of learning automaton to neural network synthesis. Conclusion. Nomenclature. References. Appendix. Index

Weighted Automata Algorithms

Abstract: Weighted automata and transducers are widely used in modern applications in bioinformatics and text, speech, and image processing. This chapter describes several fundamental weighted automata and shortest-distance algorithms including composition, determinization, minimization, and synchronization, as well as single-source and all-pairs shortest distance algorithms over general semirings. It presents the pseudocode of these algorithms, gives an analysis of their running time complexity, and illustrates their use in some simple cases. Many other complex weighted automata and transducer algorithms used in practice can be obtained by combining these core algorithms.

Efficient descriptor-vector multiplications in stochastic automata networks

Abstract: This paper examines numerical issues in computing solutions to networks of stochastic automata. It is well-known that when the matrices that represent the automata contain only constant values, the cost of performing the operation basic to all iterative solution methods, that of matrix-vector multiply, is given by ρN = ΠNi-1 ni × ΣNi=1 ni, where ni is the number of states in the ith automaton and N is the number of automata in the network. We introduce the concept of a generalized tensor product and prove a number of lemmas concerning this product. The result of these lemmas allows us to show that this relatively small number of operations is sufficient in many practical cases of interest in which the automata contain functional and not simply constant transitions. Furthermore, we show how the automata should be ordered to achieve this.

An Efficient and Scalable Semiconductor Architecture for Parallel Automata Processing

Abstract: We present the design and development of the automata processor, a massively parallel non-von Neumann semiconductor architecture that is purpose-built for automata processing. This architecture can directly implement non-deterministic finite automata in hardware and can be used to implement complex regular expressions, as well as other types of automata which cannot be expressed as regular expressions. We demonstrate that this architecture exceeds the capabilities of high-performance FPGA-based implementations of regular expression processors. We report on the development of an XML-based language for describing automata for easy compilation targeted to the hardware. The automata processor can be effectively utilized in a diverse array of applications driven by pattern matching, such as cyber security and computational biology.

The Impressive Power of Stopwatches

Abstract: In this paper we define and study the class of stopwatch automata which are timed automata augmented with stopwatches and unobservable behaviour. In particular, we investigate the expressive power of this class of automata, and show as a main result that any finite or infinite timed language accepted by a linear hybrid automaton is also acceptable by a stopwatch automaton. The consequences of this result are two-fold: firstly, it shows that the seemingly minor upgrade from timed automata to stopwatch automata immediately yields the full expressive power of linear hybrid automata. Secondly, reachability analysis of linear hybrid automata may effectively be reduced to reachability analysis of stopwatch automata. This, in turn, may be carried out using an easy (over-approximating) extension of the efficient reachability analysis for timed automata to stopwatch automata. We report on preliminary experiments on analyzing translations of linear hybrid automata using a stopwatch-extension of the real-time verification tool UPPAAL.