Almost all the knowledge empowered applications rely upon accurate knowledge,
which has to be either collected manually with high cost, or extracted
automatically with unignorable errors. In this paper, we study 20 Questions, an
online interactive game where each question-response pair corresponds to a fact
of the target entity, to acquire highly accurate knowledge effectively with
nearly zero labor cost. Knowledge acquisition via 20 Questions predominantly
presents two challenges to the intelligent agent playing games with human
players. The first one is to seek enough information and identify the target
entity with as few questions as possible, while the second one is to leverage
the remaining questioning opportunities to acquire valuable knowledge
effectively, both of which count on good questioning strategies. To address
these challenges, we propose the Learning-to-Ask (LA) framework, within which
the agent learns smart questioning strategies for information seeking and
knowledge acquisition by means of deep reinforcement learning and generalized
matrix factorization respectively. In addition, a Bayesian approach to
represent knowledge is adopted to ensure robustness to noisy user responses.
Simulating experiments on real data show that LA is able to equip the agent
with effective questioning strategies, which result in high winning rates and
rapid knowledge acquisition. Moreover, the questioning strategies for
information seeking and knowledge acquisition boost the performance of each
other, allowing the agent to start with a relatively small knowledge set and
quickly improve its knowledge base in the absence of constant human
supervision.

本文提出了一种基于 20 Questions 策略、采用 Learning-to-Ask 框架结合深度强化学习和概率矩阵分解的方法，可以有效地实现无人值守高准确度知识获取。基于实验数据结果，Learning-to-Ask 方法成功地使得智能体获得高成功率和快速知识获取的效果。