Guest editorial learning automata: theory, paradigms, and applications

Obaidat, Mohammad S.; Papadimitriou, Georgios I.; Pomportsis, A.S.

doi:10.1109/tsmcb.2002.1049604

Cited by 116 publications

(55 citation statements)

References 22 publications

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“…Learning Automata (LA) have been used in systems that have incomplete knowledge of the environment in which they operate [1,18,25,26,27,32,39]. The learning mechanism attempts to learn from a stochastic Teacher which models the Environment.…”

Section: Meta-learning Algorithm Based On Learning Automatamentioning

confidence: 99%

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

Goodwin

Yazidi

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract.Demand peaks in electrical power consumptions pose serious challenges for energy companies as these are typically unforeseen and require the net to support abnormally high consumption levels. Such peaks can be regulated in smart energy grids with the introduction of relatively simple techniques such as load balancing and smart pricing strategies. This is, however, difficult in practice because it requires prediction of peaks prior to their actual occurrence.While most studies formulate the problem as an estimation problem, we take a radically different approach and formulate it as a classical pattern recognition problem. Further, the paper applies classification methods to solve the problem and applies these with real-life data from a Norwegian smart grid pilot project. Some of the key findings are that the algorithms can accurately detect 80% of energy consumption peaks up to one week ahead of time. Furthermore, we introduce a novel Learning Automata based approaches for selecting the optimal prediction model from a pool of models in an online fashion.

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Section: Meta-learning Algorithm Based On Learning Automatamentioning

confidence: 99%

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

Goodwin

Yazidi

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…By learning to choose the optimal action, the automaton adapts itself to the environment. Excellent references that survey the field are the books by Lakshmivarahan [8], Najim and Poznyak [15], Narendra and Thathachar [16], and a recent special issue of the journal IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, PART B [17].…”

Section: Lamentioning

confidence: 99%

“…The learning paradigm, as modeled by LA, has found applications in systems that posses incomplete knowledge about the environment in which they operate [8], [15]- [17]. A variety of applications 2 that use LA have been reported in the literature.…”

Section: Lamentioning

confidence: 99%

“…This is where the ingenuity of the researcher comes into the picture-this is often a thoughtprovoking task. In the first LA designs, the transition and the output functions were time invariant, and for this reason, these LAs were considered "fixed-structure" automata [16], [17]. Tsetlin, Krylov, and Krinsky presented notable examples of this type of automata.…”

Section: Lamentioning

confidence: 99%

“…In the definition of a VSSA, the LA is completely defined by a set of actions (one of which is the output of the automaton), a set of inputs (one of which is usually the response of the environment), and a learning algorithm T . Within the context of this paper, the VSSA [8], [15]- [17] operates on a vector, called the action probability vector P(t), where…”

Section: Lamentioning

confidence: 99%

See 2 more Smart Citations

Random Early Detection for Congestion Avoidance in Wired Networks: A Discretized Pursuit Learning-Automata-Like Solution

Misra

Oommen

Yanamandra

et al. 2010

IEEE Trans. Syst., Man, Cybern. B

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Abstract-In this paper, we present a learning-automata-like 1 (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 congestion-avoidance mechanisms, augmented with a LAL philosophy. The primary objective of LALRED is to optimize the value of the average size of the queue used for congestion avoidance and to consequently reduce the total loss of packets at the queue. We attempt to achieve this by stationing a LAL algorithm at the gateways and by discretizing the probabilities of the corresponding actions of the congestion-avoidance algorithm. At every time instant, the LAL scheme, in turn, chooses the action that possesses the maximal ratio between the number of times the chosen action is rewarded and the number of times that it has been chosen. In LALRED, we simultaneously increase the likelihood of the scheme converging to the action, which minimizes the number of packet drops at the gateway. Our approach helps to improve the performance of congestion avoidance by adaptively minimizing the queue-loss rate and the average queue size. Simulation results obtained using NS2 establish the improved performance of LALRED over the traditional RED methods which were chosen as the benchmarks for performance comparison purposes.Index Terms-Average queue size, discretized pursuit learning, queue loss, random early detection (RED), stochastic learning automata (LA).

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Application‐Oriented Learning Automata

2023

Learning Automata and Their Applications to Intelligent Systems

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Guest editorial learning automata: theory, paradigms, and applications

Cited by 116 publications

References 22 publications

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

A Pattern Recognition Approach for Peak Prediction of Electrical Consumption

Random Early Detection for Congestion Avoidance in Wired Networks: A Discretized Pursuit Learning-Automata-Like Solution

Application‐Oriented Learning Automata

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