Adaptive Cascade Submodular Maximization

TL;DR

This paper introduces the adaptive cascade submodular maximization problem, considering item continuation probabilities and their impact on sequence selection to maximize expected utility, with a novel approximation algorithm.

Contribution

It formulates a new adaptive cascade submodular maximization problem incorporating continuation probabilities and develops a $0.12$ approximation algorithm.

Findings

The problem captures externalities via continuation probabilities.

Many practical applications satisfy adaptive cascade submodularity.

A $0.12$ approximation algorithm is proposed for the problem.

Abstract

In this paper, we propose and study the cascade submodular maximization problem under the adaptive setting. The input of our problem is a set of items, each item is in a particular state (i.e., the marginal contribution of an item) which is drawn from a known probability distribution. However, we can not know its actual state before selecting it. As compared with existing studies on stochastic submodular maximization, one unique setting of our problem is that each item is associated with a continuation probability which represents the probability that one is allowed to continue to select the next item after selecting the current one. Intuitively, this term captures the externality of selecting one item to all its subsequent items in terms of the opportunity of being selected. Therefore, the actual set of items that can be selected by a policy depends on the specific ordering it adopts to select items, this makes our problem fundamentally different from classical submodular set optimization problems. Our objective is to identify the best sequence of selecting items so as to maximize the expected utility of the selected items. We propose a class of stochastic utility functions, \emph{adaptive cascade submodular functions}, and show that the objective functions in many practical application domains satisfy adaptive cascade submodularity. Then we develop a $0.12$ approximation algorithm to the adaptive cascade submodular maximization problem.