Abstract
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.
The first author was partially supported by NSERC, the Natural Sciences and Engineering Research Council of Canada. This work was generously supported by KOSEF, the Korea Science and Engineering Foundation (F01-2006-000-10008-0).
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Oommen, B.J., Kim, SW., Samuel, M., Granmo, OC. (2007). Stochastic Point Location in Non-stationary Environments and Its Applications. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_84
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DOI: https://doi.org/10.1007/978-3-540-73325-6_84
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