Stochastic Submodular Probing with State-Dependent Costs

TL;DR

This paper introduces a new stochastic submodular maximization problem with state-dependent costs and rejections, providing a constant approximation solution and extending it to an online setting.

Contribution

It formulates a novel stochastic submodular probing problem with state-dependent costs and rejections, and offers a constant approximation algorithm with online extension.

Findings

Developed a constant approximation algorithm for the problem.

Extended the solution framework to an online setting.

Demonstrated the effectiveness of the approach through theoretical analysis.

Abstract

In this paper, we study a new stochastic submodular maximization problem with state-dependent costs and rejections. The input of our problem is a budget constraint $B$, and a set of items whose states (i.e., the marginal contribution and the cost of an item) are drawn from a known probability distribution. The only way to know the realized state of an item is to probe that item. We allow rejections, i.e., after probing an item and knowing its actual state, we must decide immediately and irrevocably whether to add that item to our solution or not. Our objective is to sequentially probe/selet a best group of items subject to a budget constraint on the total cost of the selected items. We present a constant approximate solution to this problem. We show that our solution can be extended to an online setting.