Human attention is a scarce resource in modern computing. A multitude of
microtasks vie for user attention to crowdsource information, perform momentary
assessments, personalize services, and execute actions with a single touch. A
lot gets done when these tasks take up the invisible free moments of the day.
However, an interruption at an inappropriate time degrades productivity and
causes annoyance. Prior works have exploited contextual cues and behavioral
data to identify interruptibility for microtasks with much success. With Quick
Question, we explore use of reinforcement learning (RL) to schedule microtasks
while minimizing user annoyance and compare its performance with supervised
learning. We model the problem as a Markov decision process and use Advantage
Actor Critic algorithm to identify interruptible moments based on context and
history of user interactions. In our 5-week, 30-participant study, we compare
the proposed RL algorithm against supervised learning methods. While the mean
number of responses between both methods is commensurate, RL is more effective
at avoiding dismissal of notifications and improves user experience over time.

使用强化学习技术调度微任务，最小化用户打扰和提高用户体验

使用强化学习在微任务中中断用户

Quick Question: Interrupting Users for Microtasks with Reinforcement  Learning

Motivation: Bioinformatics is faced with a variety of problems that require
human involvement. Tasks like genome annotation, image analysis, knowledge-base
construction and protein structure determination all benefit from human input.
In some cases people are needed in vast quantities while in others we need just
a few with very rare abilities. Crowdsourcing encompasses an emerging
collection of approaches for harnessing such distributed human intelligence.
Recently, the bioinformatics community has begun to apply crowdsourcing in a
variety of contexts, yet few resources are available that describe how these
human-powered systems work and how to use them effectively in scientific
domains. Results: Here, we provide a framework for understanding and applying
several different types of crowdsourcing. The framework considers two broad
classes: systems for solving large-volume 'microtasks' and systems for solving
high-difficulty 'megatasks'. Within these classes, we discuss system types
including: volunteer labor, games with a purpose, microtask markets and open
innovation contests. We illustrate each system type with successful examples in
bioinformatics and conclude with a guide for matching problems to crowdsourcing
solutions.

本文介绍了如何理解和应用不同类型的众包系统，包括协作劳动、有目的性游戏、微任务市场和开放式竞赛，以及这些系统在生物信息学方面的成功案例和指南。