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Article Dans Une Revue Journal of Computer and System Sciences Année : 2024

Online Computation with Untrusted Advice

Spyros Angelopoulos
Shendan Jin
  • Fonction : Auteur
Shahin Kamali
  • Fonction : Auteur
Marc Renault
  • Fonction : Auteur

Résumé

We study a generalization of the advice complexity model of online computation in which the advice is provided by an untrusted source. Our objective is to quantify the impact of untrusted advice so as to design and analyze online algorithms that are robust if the advice is adversarial, and efficient is the advice is foolproof. We focus on four well-studied online problems, namely ski rental, online bidding, bin packing and list update. For ski rental and online bidding, we show how to obtain algorithms that are Pareto-optimal with respect to the competitive ratios achieved, whereas for bin packing and list update, we give online algorithms with worst-case tradeoffs in their competitiveness, depending on whether the advice is trusted or adversarial. More importantly, we demonstrate how to prove lower bounds, within this model, on the tradeoff between the number of advice bits and the competitiveness of any online algorithm.

Dates et versions

hal-04549692 , version 1 (17-04-2024)

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Spyros Angelopoulos, Christoph Dürr, Shendan Jin, Shahin Kamali, Marc Renault. Online Computation with Untrusted Advice. Journal of Computer and System Sciences, In press. ⟨hal-04549692⟩
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