A Survey of Knowledge-based Sequential Decision Making under Uncertainty

TL;DR

This survey reviews how knowledge-based reasoning and sequential decision-making techniques can be integrated under uncertainty, highlighting recent advances, open challenges, and future research directions in artificial intelligence.

Contribution

It provides a comprehensive overview of algorithms combining declarative knowledge reasoning with sequential decision-making under uncertainty, emphasizing their complementary strengths.

Findings

Identifies key algorithms integrating RDK and SDM.

Highlights open problems and research gaps.

Suggests future directions for combining knowledge reasoning with decision-making.

Abstract

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.