Search results for "Learning automata"
showing 10 items of 76 documents
A Conclusive Analysis of the Finite-Time Behavior of the Discretized Pursuit Learning Automaton
2019
Author's accepted version (post-print). © 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Available from 20/03/2021. This paper deals with the finite-time analysis (FTA) of learning automata (LA), which is a topic for which very little work has been reported in the literature. This is as opposed to the asymptotic steady-state analysis for which there are, probabl…
Random Early Detection for Congestion Avoidance in Wired Networks: A Discretized Pursuit Learning-Automata-Like Solution
2010
Published version of an article in the journal: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works In this paper, we present a learning-automata-like (LAL) mechanism for congestion avoidance in wired networks. Our algorithm, named as LAL random early detection (LALRED), is founded on the principles of the operations of existing RED con…
On Distinguishing between Reliable and Unreliable Sensors Without a Knowledge of the Ground Truth
2015
In many applications, data from different sensors are aggregated in order to obtain more reliable information about the process that the sensors are monitoring. However, the quality of the aggregated information is intricately dependent on the reliability of the individual sensors. In fact, unreliable sensors will tend to report erroneous values of the ground truth, and thus degrade the quality of the fused information. Finding strategies to identify unreliable sensors can assist in having a counter-effect on their respective detrimental influences on the fusion process, and this has has been a focal concern in the literature. The purpose of this paper is to propose a solution to an extreme…
On Solving the Problem of Identifying Unreliable Sensors Without a Knowledge of the Ground Truth: The Case of Stochastic Environments.
2017
The purpose of this paper is to propose a solution to an extremely pertinent problem, namely, that of identifying unreliable sensors (in a domain of reliable and unreliable ones) without any knowledge of the ground truth. This fascinating paradox can be formulated in simple terms as trying to identify stochastic liars without any additional information about the truth. Though apparently impossible, we will show that it is feasible to solve the problem, a claim that is counterintuitive in and of itself. One aspect of our contribution is to show how redundancy can be introduced, and how it can be effectively utilized in resolving this paradox. Legacy work and the reported literature (for exam…
A Learning Automata Based Solution to Service Selection in Stochastic Environments
2010
Published version of a paper published in the book: Trends in Applied Intelligent Systems. Also available on SpringerLink: http://dx.doi.org/10.1007/978-3-642-13033-5_22 With the abundance of services available in today’s world, identifying those of high quality is becoming increasingly difficult. Reputation systems can offer generic recommendations by aggregating user provided opinions about service quality, however, are prone to ballot stuffing and badmouthing . In general, unfair ratings may degrade the trustworthiness of reputation systems, and changes in service quality over time render previous ratings unreliable. In this paper, we provide a novel solution to the above problems based …
On Using “Stochastic Learning on the Line” to Design Novel Distance Estimation Methods for Three-Dimensional Environments
2019
We consider the unsolved problem of Distance Estimation (DE) when the inputs are the x and y coordinates (i.e., the latitudinal and longitudinal positions) of the points under consideration, and the elevation/altitudes of the points specified, for example, in terms of their z coordinates (3DDE). The aim of the problem is to yield an accurate value for the real (road) distance between the points specified by all the three coordinates of the cities in question (This is a typical problem encountered in a GISs and GPSs.). In our setting, the distance between any pair of cities is assumed to be computed by merely having access to the coordinates and known inter-city distances of a small subset o…
Discretized Bayesian Pursuit – A New Scheme for Reinforcement Learning
2012
Published version of a chapter in the book: Advanced Research in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-31087-4_79 The success of Learning Automata (LA)-based estimator algorithms over the classical, Linear Reward-Inaction ( L RI )-like schemes, can be explained by their ability to pursue the actions with the highest reward probability estimates. Without access to reward probability estimates, it makes sense for schemes like the L RI to first make large exploring steps, and then to gradually turn exploration into exploitation by making progressively smaller learning steps. However, this behavior becomes counter-intuitive wh…
A Learning Automata Local Contribution Sampling Applied to Hydropower Production Optimisation
2017
Learning Automata (LA) is a powerful approach for solving complex, non-linear and stochastic optimisation problems. However, existing solutions struggle with high-dimensional problems due to slow convergence, arguably caused by the global nature of feedback. In this paper we introduce a novel Learning Automata (LA) scheme to attack this challenge. The scheme is based on a parallel form of Local Contribution Sampling (LCS), which means that the LA receive individually directed feedback, designed to speed up convergence. Furthermore, our scheme is highly decentralized, allowing parallel execution on GPU architectures. To demonstrate the power of our scheme, the LA LCS is applied to hydropower…
Learning Automata-Based Solutions to the Multi-Elevator Problem
2019
In the last century, AI has been the topic of interest in many areas, where the focus was on mimicking human behaviour. It has been researched to be incorporated into different domains, such as security, diagnosis, autonomous driving, financial prediction analysis and playing games such as chess and Go. They also worked on different subfields of AI such as machine learning, deep learning, pattern recognition and other relevant subfields. Our work in a previous paper [1] focused on a problem that has not been tackled using AI before, which is the elevator-problem. In which we try to find the optimal parking floor for the elevator for the single elevator problem. In this paper, our work exten…
A Hierarchy of Twofold Resource Allocation Automata Supporting Optimal Sampling
2009
We consider the problem of allocating limited sampling resources in a "real-time" manner with the purpose of estimating multiple binomial proportions. More specifically, the user is presented with `n ' sets of data points, S 1 , S 2 , ..., S n , where the set S i has N i points drawn from two classes {*** 1 , *** 2 }. A random sample in set S i belongs to *** 1 with probability u i and to *** 2 with probability 1 *** u i , with {u i }. i = 1, 2, ...n , being the quantities to be learnt. The problem is both interesting and non-trivial because while both n and each N i are large, the number of samples that can be drawn is bounded by a constant, c . We solve the problem by first modelling it a…