Associative learning of Boolean functions

Mukhopadhyay, Soumik; Thathachar, M. A. L.

doi:10.1109/21.44015

Cited by 18 publications

(7 citation statements)

References 5 publications

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“…The reinforcement learning algorithm used in this paper is called Pursuit Learning, which is a model-based algorithm and assumes a stationary environment [21]. The learning is not content-sensitive as it disregards the diversity of document contents streamed at each agent.…”

Section: Learning Algorithm: Pursuit Learningmentioning

confidence: 99%

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Collaborative classifier agents

Mostafa

2007

Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries

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We developed a multi-agent framework where agents had limited/distributed knowledge for document classification and collaborated with each other to overcome the knowledge distribution. Each agent was equipped with a certain learning algorithm for predicting potential collaborators, or helping agents. We conducted experimental research on a standard news corpus to examine the impact of two learning algorithms: Pursuit Learning and Nearest Centroid Learning. For a fundamental retrieval operation, namely classification, both algorithms achieved competitive classification effectiveness and efficiency. Subsequently, the impact of the learning exploration rate and the maximum collaboration range on classification effectiveness and efficiency were examined. Close investigation of agent learning dynamics revealed increasing and stabilizing patterns that were enhanced by the learning algorithms.

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Section: Learning Algorithm: Pursuit Learningmentioning

confidence: 99%

“…This, in turn, will result in the convergence of the P vector to E, and hence, the optimal action. The convergence property of the Pursuit Algorithm has been proven [21].…”

Section: Learning Algorithm: Pursuit Learningmentioning

confidence: 99%

Collaborative classifier agents

Mostafa

2007

Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries

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“…the adaption to the environment. LAs have found a broad range of applications reported in the literature, such as game playing [2]- [4], pattern recognition [5], classification [6], knapsack problem [7], tutorial-like system [8], [9], object partitioning [10]- [13], cellular automata [14], telephony routing [15], [16], scheduling [17], minimum-spanning circle problem [18], congestion avoidance [19], function optimization [20], [21], resource allocation and assignment problems [22]- [24], automaton controller [25], control absorption columns, flexible manufacturing plants, and other applications such as dryers, vehicles, irrigation canals, multimedia network, robots, liquid-liquid extraction columns, bioreactors, distributed fuzzy logic processors, image processing, and data compression. Various LAs, their properties and applications have been reviewed in survey papers [26], [27] and books [28]- [32].…”

Section: Introductionmentioning

confidence: 99%

Fast and Epsilon-Optimal Discretized Pursuit Learning Automata

Zhang

Wang

Zhou

2015

IEEE Trans. Cybern.

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Learning automata (LA) are powerful tools for reinforcement learning. A discretized pursuit LA is the most popular one among them. During an iteration its operation consists of three basic phases: 1) selecting the next action; 2) finding the optimal estimated action; and 3) updating the state probability. However, when the number of actions is large, the learning becomes extremely slow because there are too many updates to be made at each iteration. The increased updates are mostly from phases 1 and 3. A new fast discretized pursuit LA with assured ε -optimality is proposed to perform both phases 1 and 3 with the computational complexity independent of the number of actions. Apart from its low computational complexity, it achieves faster convergence speed than the classical one when operating in stationary environments. This paper can promote the applications of LA toward the large-scale-action oriented area that requires efficient reinforcement learning tools with assured ε -optimality, fast convergence speed, and low computational complexity for each iteration.

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“…Oommen and Lanctot [13] proposed a discretized pursuit automata (DPA) model and to improve both the convergence speed and the computation time requirement. The main applications of these automata models are in parameter optimization [7,15], game theory [11], telephone traffic routing [9,12], task scheduling [11] and associative learning [8].…”

Section: Introductionmentioning

confidence: 99%

Multi‐modal parameter identification by automata approach

Huang

Wang

Kuo

1993

Journal of the Chinese Institute of Engineers

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In this paper, we consider the multi-modal function optimization problem. An automata model with improved learning schemes is proposed to solve the global optimization problem. Theoretically, we prove that the automaton converges to the global optimum with a probability arbitrarily close to 1. The numerical simulation results show that the automata approach is better than both the well-known gradient approach and the simulated annealing method. The simulation results also show that our automata model converges faster than the other existing models in the literature.^ i m

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Associative learning of Boolean functions

Cited by 18 publications

References 5 publications

Collaborative classifier agents

Collaborative classifier agents

Fast and Epsilon-Optimal Discretized Pursuit Learning Automata

Multi‐modal parameter identification by automata approach

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