# A Survey of Knowledge-based Sequential Decision Making under Uncertainty

arXiv: https://arxiv.org/abs/2008.08548 · HTML: https://mlsanity.com/paper/2008.08548/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [A Survey on Interpretable Reinforcement Learning](https://mlsanity.com/paper/2112.13112/) (2021)
- [The Logical Options Framework](https://mlsanity.com/paper/2102.12571/) (2021)
- [Reasoning with Scene Graphs for Robot Planning under Partial Observability](https://mlsanity.com/paper/2202.10432/) (2022)
- [Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey](https://mlsanity.com/paper/2304.12090/) (2023)

## Abstract (arXiv)

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.