R\'enyi-Ulam Games and Online Computation with Imperfect Advice
Spyros Angelopoulos, Shahin Kamali

TL;DR
This paper explores online algorithms with imperfect advice, connecting them to Rényi-Ulam games, and provides new bounds and techniques for handling advice errors in various online problems.
Contribution
It establishes the first lower bounds for online problems with imperfect advice and links this model to fault-tolerance and resource augmentation techniques.
Findings
Derived upper and lower bounds on competitive ratios.
Connected online advice models to Rényi-Ulam games.
Introduced methods to remove dependence on error tolerance.
Abstract
We study the nascent setting of online computation with imperfect advice, in which the online algorithm is enhanced by some prediction encoded in the form of a possibly erroneous binary string. The algorithm is oblivious to the advice error, but defines a desired tolerance, namely an upper bound on the number of erroneous advice bits it can tolerate. This is a model that generalizes the untrusted advice model [Angelopoulos et al. ITCS 2020], in which the performance of the algorithm is only evaluated at the extreme values of error (namely, if the advice has either no errors, or if it is generated adversarially). In this work, we establish connections between games with a lying responder, also known as R\'enyi-Ulam games, and the design and analysis of online algorithms with imperfect advice. Specifically, we demonstrate how to obtain upper and lower bounds on the competitive ratio for…
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