Bj. Oommen et G. Raghunath, AUTOMATA LEARNING AND INTELLIGENT TERTIARY SEARCHING FOR STOCHASTIC POINT LOCATION, IEEE transactions on systems, man and cybernetics. Part B. Cybernetics, 28(6), 1998, pp. 947-954
Consider the problem of a robot (learning mechanism or algorithm) atte
mpting to locate a point on a line. The mechanism interacts with a ran
dom environment which essentially informs it, possibly erroneously, wh
ich way it should move. The first reported paper to solve this problem
[14] presented a solution which operated in a discretized space. In t
his paper we present a new scheme by which the point can be learnt usi
ng a combination of various learning principles. The heart of the stra
tegy involves performing a controlled random walk on the underlying sp
ace and then intelligently pruning the space using an adaptive tertiar
y search, The overall learning scheme is shown to be E-optimal. Just a
s in the case of the results presented in [14] the application of the
solution in nonlinear optimization has been alluded to. In a typical o
ptimization process the algorithm has to work its way toward the maxim
um (minimum) using local information. However, the crucial issue in th
ese strategies is that of determining the parameter to be used in the
optimization itself, If the parameter is too small the convergence is
sluggish. On the other hand, if the parameter is too large, the system
could erroneously converge or even oscillate. The strategy presented
here can be utilized to determine the best parameter to be used in the
optimization.