论文标题
对不确定性下基于知识的顺序决策的调查
A Survey of Knowledge-based Sequential Decision Making under Uncertainty
论文作者
论文摘要
用声明知识（RDK）和顺序决策（SDM）推理是人工智能的两个关键研究领域。 RDK方法的原因是具有声明领域知识（包括常识性知识）的原因，它是先验或随着时间的收购，而SDM方法（概率计划和强化学习）试图计算在时间范围内最大程度地提高预期的累积效用的行动政策；两类方法的原因是在存在不确定性的情况下。尽管这两个领域拥有丰富的文献，但研究人员尚未完全探索他们的互补优势。在本文中，我们调查了利用RDK方法的算法，同时在不确定性下做出顺序决策。我们讨论了重大发展，开放问题和未来工作的方向。
Reasoning with declarative knowledge (RDK) and sequential decision-making (SDM) are two key research areas in artificial intelligence. RDK methods reason with declarative domain knowledge, including commonsense knowledge, that is either provided a priori or acquired over time, while SDM methods (probabilistic planning and reinforcement learning) seek to compute action policies that maximize the expected cumulative utility over a time horizon; both classes of methods reason in the presence of uncertainty. Despite the rich literature in these two areas, researchers have not fully explored their complementary strengths. In this paper, we survey algorithms that leverage RDK methods while making sequential decisions under uncertainty. We discuss significant developments, open problems, and directions for future work.
