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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Issue 6 • Date Dec. 2002

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Displaying Results 1 - 17 of 17
  • Guest editorial learning automata: theory, paradigms, and applications

    Page(s): 706 - 709
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    Freely Available from IEEE
  • Special issue list of reviewers

    Page(s): 710
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    Freely Available from IEEE
  • List of reviewers

    Page(s): 846 - 850
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    Freely Available from IEEE
  • Author index

    Page(s): 851 - 854
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    Freely Available from IEEE
  • Subject index

    Page(s): 854 - 862
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  • Varieties of learning automata: an overview

    Page(s): 711 - 722
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (513 KB) |  | HTML iconHTML  

    Automata models of learning systems introduced in the 1960s were popularized as learning automata (LA) in a survey paper by Narendra and Thathachar (1974). Since then, there have been many fundamental advances in the theory as well as applications of these learning models. In the past few years, the structure of LA, has been modified in several directions to suit different applications. Concepts such as parameterized learning automata (PLA), generalized learning,automata (GLA), and continuous action-set learning automata (CALA) have been proposed, analyzed, and applied to solve many significant learning problems. Furthermore, groups of LA forming teams and feedforward networks have been shown to converge to desired solutions under appropriate learning algorithms. Modules of LA have been used for parallel operation with consequent increase in speed of convergence. All of these concepts and results are relatively new and are scattered in technical literature. An attempt has been made in this paper to bring together the main ideas involved in a unified framework and provide pointers to relevant references. View full abstract»

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  • The STAR automaton: expediency and optimality properties

    Page(s): 723 - 737
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    We present the STack ARchitecture (STAR) automaton. It is a fixed structure, multiaction, reward-penalty learning automaton, characterized by a star-shaped state transition diagram. Each branch of the star contains D states associated with a particular action. The branches are connected to a central "neutral" state. The most general version of STAR involves probabilistic state transitions in response to reward and/or penalty, but deterministic transitions can also be used. The learning behavior of STAR results from the stack-like operation of the branches; the learning parameter is D. By mathematical analysis, it is shown that STAR with deterministic reward/probabilistic penalty and a sufficiently large D can be rendered ε-optimal in every stationary environment. By numerical simulation it is shown that in nonstationary, switching environments, STAR usually outperforms classical variable structure automata such as LR-P, LR-I, and LR-εP. View full abstract»

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  • Genetic algorithm-based neural fuzzy decision tree for mixed scheduling in ATM networks

    Page(s): 832 - 845
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (604 KB) |  | HTML iconHTML  

    Future broadband integrated services networks based on asynchronous transfer mode (ATM) technology are expected to support multiple types of multimedia information with diverse statistical characteristics and quality of service (QoS) requirements. To meet these requirements, efficient scheduling methods are important for traffic control in ATM networks. Among general scheduling schemes, the rate monotonic algorithm is simple enough to be used in high-speed networks, but does not attain the high system utilization of the deadline driven algorithm. However, the deadline driven scheme is computationally complex and hard to implement in hardware. The mixed scheduling algorithm is a combination of the rate monotonic algorithm and the deadline driven algorithm; thus it can provide most of the benefits of these two algorithms. In this paper, we use the mixed scheduling algorithm to achieve high system utilization under the hardware constraint. Because there is no analytic method for schedulability testing of mixed scheduling, we propose a genetic algorithm-based neural fuzzy decision tree (GANFDT) to realize it in a real-time environment. The GANFDT combines a GA and a neural fuzzy network into a binary classification tree. This approach also exploits the power of the classification tree. Simulation results show that the GANFDT provides an efficient way of carrying out mixed scheduling in ATM networks. View full abstract»

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  • Generalized pursuit learning schemes: new families of continuous and discretized learning automata

    Page(s): 738 - 749
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (865 KB) |  | HTML iconHTML  

    The fastest learning automata (LA) algorithms currently available fall in the family of estimator algorithms introduced by Thathachar and Sastry (1986). The pioneering work of these authors was the pursuit algorithm, which pursues only the current estimated optimal action. If this action is not the one with the minimum penalty probability, this algorithm pursues a wrong action. In this paper, we argue that a pursuit scheme that generalizes the traditional pursuit algorithm by pursuing all the actions with higher reward estimates than the chosen action, minimizes the probability of pursuing a wrong action, and is a faster converging scheme. To attest this, we present two new generalized pursuit algorithms (GPAs) and also present a quantitative comparison of their performance against the existing pursuit algorithms. Empirically, the algorithms proposed here are among the fastest reported LA to date. View full abstract»

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  • Colonies of learning automata

    Page(s): 772 - 780
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    Originally, learning automata (LAs) were introduced to describe human behavior from both a biological and psychological point of view. In this paper, we show that a set of interconnected LAs is also able to describe the behavior of an ant colony, capable of finding the shortest path from their nest to food sources and back. The field of ant colony optimization (ACO) models ant colony behavior using artificial ant algorithms. These algorithms find applications in a whole range of optimization problems and have been experimentally proved to work very well. It turns out that a known model of interconnected LA, used to control Markovian decision problems (MDPs) in a decentralized fashion, matches perfectly with these ant algorithms. The field of LAs can thus both impart in the understanding of why ant algorithms work so well and may also become an important theoretical tool for learning in multiagent systems (MAS) in general. To illustrate this, we give an example of how LAs can be used directly in common Markov game problems. View full abstract»

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  • Learning automata-based bus arbitration for shared-medium ATM switches

    Page(s): 815 - 820
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (414 KB) |  | HTML iconHTML  

    Although new high-bandwidth network technologies are being introduced and widely deployed, asynchronous transfer mode (ATM) is still considered one of the most important network technologies currently in use. A number of ATM switch architectures have been proposed in the literature. However, industry has shown that is better to use the well-known shared-medium technique in the design of these ATM switches. In this paper, four variations of a new distributed scheme are proposed for the arbitration of a shared bus of an ATM switch. These schemes are based on learning automata. By taking advantage of the bursty nature of ATM traffic, the new arbitration scheme demonstrates superb performance compared to the time division multiple access (TDMA) scheme. View full abstract»

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  • A new learning algorithm for the hierarchical structure learning automata operating in the nonstationary S-model random environment

    Page(s): 750 - 758
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    An extended algorithm of the relative reward strength algorithm is proposed. It is shown that the proposed algorithm ensures the convergence with probability I to the optimal path under the certain type of nonstationary environment. Several computer simulation results confirm the effectiveness of the proposed algorithm. View full abstract»

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  • Discretized learning automata solutions to the capacity assignment problem for prioritized networks

    Page(s): 821 - 831
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    We present a discretized learning automaton (LA) solution to the capacity assignment (CA) problem which focuses on finding the best possible set of capacities for the links that satisfy the traffic requirements in a prioritized network while minimizing the cost. Most approaches consider a single class of packets flowing through the network, but in reality, different classes of packets with different average packet lengths and different priorities are transmitted over the networks. This generalized model is the focus of this paper. Although the problem is inherently NP-hard, a few approximate solutions have been proposed in the literature. Marayuma and Tang (1977) proposed a single algorithm composed of several elementary heuristic procedures. Other solutions tackle the problem by using modern-day artificial intelligence (AI) paradigms such as simulated annealing and genetic algorithms (GAs). In 2000, we introduced a new method, superior to these, that uses continuous LA. In this paper, we present a discretized LA solution to the problem. This solution uses a meta-action philosophy new to the field of LA, and is probably the best available solution to this extremely complex problem. View full abstract»

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  • Genetic learning automata for function optimization

    Page(s): 804 - 815
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    Stochastic learning automata and genetic algorithms (GAs) have previously been shown to have valuable global optimization properties. Learning automata have, however, been criticized for having a relatively slow rate of convergence. In this paper, these two techniques are combined to provide an increase in the rate of convergence for the learning automata and also to improve the chances of escaping local optima. The technique separates the genotype and phenotype properties of the GA and has the advantage that the degree of convergence can be quickly ascertained. It also provides the GA with a stopping rule. If the technique is applied to real-valued function optimization problems, then bounds on the range of the values within which the global optima is expected can be determined throughout the search process. The technique is demonstrated through a number of bit-based and real-valued function optimization examples. View full abstract»

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  • Learning through reinforcement for N-person repeated constrained games

    Page(s): 759 - 771
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    The design and analysis of an adaptive strategy for N-person averaged constrained stochastic repeated game are addressed. Each player is modeled by a stochastic variable-structure learning automaton. Some constraints are imposed on some functions of the probabilities governing the selection of the player's actions. After each stage, the payoff to each player as well as the constraints are random variables. No information concerning the parameters of the game is a priori available. The "diagonal concavity" conditions are assumed to be fulfilled to guarantee the existence and uniqueness of the Nash equilibrium. The suggested adaptive strategy which uses only the current realizations (outcomes and constraints) of the game is based on the Bush-Mosteller reinforcement scheme in connection with a normalization procedure. The Lagrange multipliers approach with a regularization is used. The asymptotic properties of this algorithm are analyzed. Simulation results illustrate the feasibility and the performance of this adaptive strategy. View full abstract»

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  • V-Lab-a virtual laboratory for autonomous agents-SLA-based learning controllers

    Page(s): 791 - 803
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    In this paper, we present the use of stochastic learning automata (SLA) in multiagent robotics. In order to fully utilize and implement learning control algorithms in the control of multiagent robotics, an environment for simulation has to be first created. A virtual laboratory for simulation of autonomous agents, called V-Lab is described. The V-Lab architecture can incorporate various models of the environment as well as the agent being trained. A case study to demonstrate the use of SLA is presented. View full abstract»

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  • On the use of learning automata in the control of broadcast networks: a methodology

    Page(s): 781 - 790
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (594 KB) |  | HTML iconHTML  

    Due to its fixed assignment nature, the well-known time division multiple access (TDMA) protocol suffers from poor performance when the offered traffic is bursty. In this paper, an adaptive TDMA protocol, which is capable of operating efficiently under bursty traffic conditions, is introduced. According to the proposed protocol, the station which is granted permission to transmit at each time slot is selected by means of learning automata (LA). The choice probability of the selected station is updated by taking into account the network feedback information. The system which consists of the LA and the network is analyzed and it is proven that the choice probability of each station asymptotically tends to be proportional to the probability that this station is not idle. Although there is no centralized control of the stations and the traffic characteristics are unknown and time-variable, each station tends to take a fraction of the bandwidth proportional to its needs. Furthermore, extensive simulation results are presented, which indicate that the proposed protocol achieves a significantly higher performance than other well-known TDMA protocols when operating under bursty traffic conditions. View full abstract»

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Aims & Scope

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics focuses on cybernetics, including communication and control across humans, machines and organizations at the structural or neural level

 

This Transaction ceased production in 2012. The current retitled publication is IEEE Transactions on Cybernetics.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Dr. Eugene Santos, Jr.
Thayer School of Engineering
Dartmouth College