Search results for "PROB"
showing 10 items of 8859 documents
Supervised learning of time-independent Hamiltonians for gate design
2018
We present a general framework to tackle the problem of finding time-independent dynamics generating target unitary evolutions. We show that this problem is equivalently stated as a set of conditions over the spectrum of the time-independent gate generator, thus transforming the task to an inverse eigenvalue problem. We illustrate our methodology by identifying suitable time-independent generators implementing Toffoli and Fredkin gates without the need for ancillae or effective evolutions. We show how the same conditions can be used to solve the problem numerically, via supervised learning techniques. In turn, this allows us to solve problems that are not amenable, in general, to direct ana…
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…
Communication complexity in a 3-computer model
1996
It is proved that the probabilistic communication complexity of the identity function in a 3-computer model isO(√n).
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…
On the Influence of Technology on Learning Processes
2014
Probabilistic computations and frequency computations were invented for the same purpose, namely, to study possible advantages of technology involving random choices. Recently several authors have discovered close relationships of these generalizations of deterministic computations to computations taking advice. Various forms of computation taking advice were studied by Karp and Lipton [1], Damm and Holzer [2], and Freivalds [3]. In the present paper, we apply the nonconstructive, probabilistic, and frequency methods to an inductive inference paradigm originally due to Gold [4] and investigate their impact on the resulting learning models. Several trade-offs with respect to the resulting l…
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…
A Problem Structuring Method
1991
Given a formal definition of problem and a formal definition of system, the equivalence between both concepts is studied. Considering a problem as a 3-tuple , where D is the set of possible data, R is the set of possible results, and P the set of conditions of the problem, classes of problems are constructed as combinations of types of data, types of results and types of conditions. For example, data can be either literal or numerical, either with uncertainty or not; conditions can be determined by rules, tables, equations, it may have uncertainty, etc. As a case of application it is outlined how some of the most common problems (knowledge representation, search, reasoning and planning, etc…
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…
Combining finite learning automata with GSAT for the satisfiability problem
2010
A large number of problems that occur in knowledge-representation, learning, very large scale integration technology (VLSI-design), and other areas of artificial intelligence, are essentially satisfiability problems. The satisfiability problem refers to the task of finding a satisfying assignment that makes a Boolean expression evaluate to True. The growing need for more efficient and scalable algorithms has led to the development of a large number of SAT solvers. This paper reports the first approach that combines finite learning automata with the greedy satisfiability algorithm (GSAT). In brief, we introduce a new algorithm that integrates finite learning automata and traditional GSAT use…