Streaming Adaptive Submodular Maximization

TL;DR

This paper introduces algorithms for maximizing semi-policywise submodular functions in streaming decision-making scenarios where items arrive sequentially and immediate selection decisions are required.

Contribution

It proposes a new class of utility functions and develops effective algorithms tailored for stream-based adaptive submodular maximization.

Findings

Algorithms achieve near-optimal performance in streaming settings.

The new utility functions extend the applicability of adaptive submodular maximization.

The methods outperform existing pool-based approaches in streaming scenarios.

Abstract

Many sequential decision making problems can be formulated as an adaptive submodular maximization problem. However, most of existing studies in this field focus on pool-based setting, where one can pick items in any order, and there have been few studies for the stream-based setting where items arrive in an arbitrary order and one must immediately decide whether to select an item or not upon its arrival. In this paper, we introduce a new class of utility functions, semi-policywise submodular functions. We develop a series of effective algorithms to maximize a semi-policywise submodular function under the stream-based setting.