Automata learning and intelligent tertiary searching for stochastic point location

—Consider the problem of a robot (learning mechanism or algorithm) attempting to locate a point on a line. The mechanism interacts with a random environment which essentially informs it, possibly erroneously, which way it should move. The first reported paper to solve this problem [14] presented a solution which operated in a discretized space. In this paper we present a new scheme by which the point can be learnt using a combination of various learning principles. The heart of the strategy involves performing a controlled random walk on the underlying space and then intelligently pruning the space using an adaptive tertiary search. The overall learning scheme is shown to be """-optimal. Just as in the case of the results presented in [14] the application of the solution in nonlinear optimization has been alluded to. In a typical optimization process the algorithm has to work its way toward the maximum (minimum) using local information. However, the crucial issue in t...

• Type: Journal
• Year: 1998
• Where: TSMC
• Authors: B. John Oommen, Govindachari Raghunath
• Keywords: Adaptive Tertiary Search, Controlled Random Walk, TSMC 1998, Various Learning Principles