Deterministic Majority filters applied to stochastic sorting
ACM-SE 42 Proceedings of the 42nd annual Southeast regional conference
Modeling and simulating a disease outbreak by learning a contagion parameter-based model
Proceedings of the 2008 Spring simulation multiconference
Stochastic point location in non-stationary environments and its applications
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Engineering Applications of Artificial Intelligence
A stochastic search on the line-based solution to discretized estimation
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
A hierarchical learning scheme for solving the stochastic point location problem
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Towards automatic assessment of government web sites
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
Adaptive step searching for solving stochastic point location problem
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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We consider the problem of a learning mechanism (for example, a robot) locating a point on a line when it is interacting with a random environment which essentially informs it, possibly erroneously, which way it should move. In this paper we present a novel scheme by which the point can he learned using some recently devised learning principles. The heart of the strategy involves discretizing the space and performing a controlled random walk on this space. The scheme is shown to be ε-optimal and to converge with probability 1. Although the problem is solved in its generality, its application in nonlinear optimization has also been suggested. Typically, an optimization process involves working one's way toward the maximum (minimum) using the local information that is available. However, the crucial issue in these 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. Our strategy can be used to determine the best parameter to be used in the optimization