Stochastic sampling in computer graphics
ACM Transactions on Graphics (TOG)
No. 318 on SWAT 88: 1st Scandinavian workshop on algorithm theory
Learning automata: an introduction
Learning automata: an introduction
An overview of representative problems in location research
Management Science
Automatic Finding of Main Roads in Aerial Images by Using Geometric-Stochastic Models and Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic properties of the random waypoint mobility model
Wireless Networks
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Prototype reduction schemes applicable for non-stationary data sets
Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automata learning and intelligent tertiary searching for stochasticpoint location
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized pursuit learning schemes: new families of continuous and discretized learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Parameter learning from stochastic teachers and stochastic compulsive liars
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper reports the first known solution to the Stochastic Point Location (SPL) problem when the Environment is non-stationary. The SPL problem [12,13,14] involves a general learning problem in which the learning mechanism attempts to learn a "parameter", say λ*, within a closed interval. However, unlike the earlier reported results, we consider the scenario when the learning is to be done in a non-stationary setting. The Environment communicates with an intermediate entity (referred to as the Teacher) about the point itself, advising it where it should go. The mechanism searching for the point, in turn, receives responses from the Teacher, directing it how it should move. Therefore, the point itself, in the overall setting, is moving, delivering possibly incorrect information about its location to the Teacher. This, in turn, means that the "Environment" is itself non-stationary, implying that the advice of the Teacher is both uncertain and changing with time - rendering the problem extremely fascinating. The heart of the strategy we propose involves discretizing the space and performing a controlled random walk on this space. Apart from deriving some analytic results about our solution, we also report simulation results which demonstrate the power of the scheme.