Search results for "Automata"
showing 10 items of 453 documents
A Tool for Implementing and Exploring SBM Models: Universal 1D Invertible Cellular Automata
2005
The easiest form of designing Cellular Automata rules with features such as invertibility or particle conserving is to rely on a partitioning scheme, the most important of which is the 2D Margolus neighborhood. In this paper we introduce a 1D Margolus-like neighborhood that gives support to a complete set of Cellular Automata models. We present a set of models called Sliding Ball Models based on this neighborhood and capable of universal computation. We show the way of designing logic gates with these models, propose a digital structure to implement them and finally we present SBMTool, a software development system capable of working with the new models.
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing
2020
The Tsetlin Machine (TM) is a machine learning algorithm founded on the classical Tsetlin Automaton (TA) and game theory. It further leverages frequent pattern mining and resource allocation principles to extract common patterns in the data, rather than relying on minimizing output error, which is prone to overfitting. Unlike the intertwined nature of pattern representation in neural networks, a TM decomposes problems into self-contained patterns, represented as conjunctive clauses. The clause outputs, in turn, are combined into a classification decision through summation and thresholding, akin to a logistic regression function, however, with binary weights and a unit step output function. …
Shrinking language models by robust approximation
2002
We study the problem of reducing the size of a language model while preserving recognition performance (accuracy and speed). A successful approach has been to represent language models by weighted finite-state automata (WFAs). Analogues of classical automata determinization and minimization algorithms then provide a general method to produce smaller but equivalent WFAs. We extend this approach by introducing the notion of approximate determinization. We provide an algorithm that, when applied to language models for the North American Business task, achieves 25-35% size reduction compared to previous techniques, with negligible effects on recognition time and accuracy.
Algorithmic Analysis of Programs with Well Quasi-ordered Domains
2000
AbstractOver the past few years increasing research effort has been directed towards the automatic verification of infinite-state systems. This paper is concerned with identifying general mathematical structures which can serve as sufficient conditions for achieving decidability. We present decidability results for a class of systems (called well-structured systems) which consist of a finite control part operating on an infinite data domain. The results assume that the data domain is equipped with a preorder which is a well quasi-ordering, such that the transition relation is “monotonic” (a simulation) with respect to the preorder. We show that the following properties are decidable for wel…
Quantum Computers and Quantum Automata
2000
Quantum computation is a most challenging project involving research both by physicists and computer scientists. The principles of quantum computation differ from the principles of classical computation very much. When quantum computers become available, the public-key cryptography will change radically. It is no exaggeration to assert that building a quantum computer means building a universal code-breaking machine. Quantum finite automata are expected to appear much sooner. They do not generalize deterministic finite automata. Their capabilities are incomparable.
On enhancing the object migration automaton using the Pursuit paradigm
2017
Abstract One of the most difficult problems that is all-pervasive in computing is that of partitioning. It has applications in the partitioning of databases into relations, the realization of the relations themselves into sub-relations based on the partitioning of the attributes, the assignment of processes to processors, graph partitioning, and the task assignment problem, etc. The problem is known to be NP-hard. The benchmark solution for this for the Equi-Partitioning Problem (EPP) has involved the classic field of Learning Automata (LA), and the corresponding algorithm, the Object Migrating Automata (OMA) has been used in all of these application domains. While the OMA is a fixed struct…
The Hierarchical Continuous Pursuit Learning Automation: A Novel Scheme for Environments With Large Numbers of Actions.
2019
Although the field of learning automata (LA) has made significant progress in the past four decades, the LA-based methods to tackle problems involving environments with a large number of actions is, in reality, relatively unresolved. The extension of the traditional LA to problems within this domain cannot be easily established when the number of actions is very large. This is because the dimensionality of the action probability vector is correspondingly large, and so, most components of the vector will soon have values that are smaller than the machine accuracy permits, implying that they will never be chosen . This paper presents a solution that extends the continuous pursuit paradigm to …
The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions
2018
Part 10: Learning - Intelligence; International audience; Although the field of Learning Automata (LA) has made significant progress in the last four decades, the LA-based methods to tackle problems involving environments with a large number of actions are, in reality, relatively unresolved. The extension of the traditional LA (fixed structure, variable structure, discretized, and pursuit) to problems within this domain cannot be easily established when the number of actions is very large. This is because the dimensionality of the action probability vector is correspondingly large, and consequently, most components of the vector will, after a relatively short time, have values that are smal…
On Utilizing Stochastic Non-linear Fractional Bin Packing to Resolve Distributed Web Crawling
2014
This paper deals with the extremely pertinent problem of web crawling, which is far from trivial considering the magnitude and all-pervasive nature of the World-Wide Web. While numerous AI tools can be used to deal with this task, in this paper we map the problem onto the combinatorially-hard stochastic non-linear fractional knapsack problem, which, in turn, is then solved using Learning Automata (LA). Such LA-based solutions have been recently shown to outperform previous state-of-the-art approaches to resource allocation in Web monitoring. However, the ever growing deployment of distributed systems raises the need for solutions that cope with a distributed setting. In this paper, we prese…
User Grouping and Power Allocation in NOMA Systems: A Reinforcement Learning-Based Solution
2020
In this paper, we present a pioneering solution to the problem of user grouping and power allocation in Non-Orthogonal Multiple Access (NOMA) systems. There are two fundamentally salient and difficult issues associated with NOMA systems. The first involves the task of grouping users together into the pre-specified time slots. The subsequent second phase augments this with the solution of determining how much power should be allocated to the respective users. We resolve this with the first reported Reinforcement Learning (RL)-based solution, which attempts to solve the partitioning phase of this issue. In particular, we invoke the Object Migration Automata (OMA) and one of its variants to re…