Robust Reward Placement under Uncertainty

TL;DR

This paper introduces the Robust Reward Placement problem (RRP), addressing the challenge of optimally placing rewards in uncertain network environments with adversarial mobility patterns, and provides algorithms with theoretical guarantees.

Contribution

It formulates RRP as a new NP-hard problem, proves its inapproximability, and develops a pseudo-polynomial algorithm with approximation guarantees, along with heuristics.

Findings

The RRP is NP-hard and inapproximable.

The $ ext{ extbackslash}Psi$-Saturate algorithm achieves an $ ext{ extbackslash}epsilon$-additive approximation.

Heuristics perform well on synthetic and real data.

Abstract

We consider a problem of placing generators of rewards to be collected by randomly moving agents in a network. In many settings, the precise mobility pattern may be one of several possible, based on parameters outside our control, such as weather conditions. The placement should be robust to this uncertainty, to gain a competent total reward across possible networks. To study such scenarios, we introduce the Robust Reward Placement problem (RRP). Agents move randomly by a Markovian Mobility Model with a predetermined set of locations whose connectivity is chosen adversarially from a known set $\Pi$ of candidates. We aim to select a set of reward states within a budget that maximizes the minimum ratio, among all candidates in $\Pi$, of the collected total reward over the optimal collectable reward under the same candidate. We prove that RRP is NP-hard and inapproximable, and develop $\Psi$-Saturate, a pseudo-polynomial time algorithm that achieves an $\epsilon$-additive approximation by exceeding the budget constraint by a factor that scales as $O(\ln |\Pi|/\epsilon)$. In addition, we present several heuristics, most prominently one inspired by a dynamic programming algorithm for the max-min 0-1 KNAPSACK problem. We corroborate our theoretical analysis with an experimental evaluation on synthetic and real data.