AI Planning: A Primer and Survey (Preliminary Report)

TL;DR

This paper provides a comprehensive overview of AI planning, covering classical concepts, extensions for uncertainty and time, state-of-the-art solving techniques, and learning approaches for structure and generalization.

Contribution

It offers a non-exhaustive primer and survey that bridges knowledge gaps between AI planning and other decision-making sub-disciplines.

Findings

Classical AI planning concepts are foundational across decision-making fields.

State-of-the-art techniques effectively exploit problem structure for improved solutions.

Learning methods enable structure extraction and generalization in AI planning.

Abstract

Automated decision-making is a fundamental topic that spans multiple sub-disciplines in AI: reinforcement learning (RL), AI planning (AP), foundation models, and operations research, among others. Despite recent efforts to ``bridge the gaps'' between these communities, there remain many insights that have not yet transcended the boundaries. Our goal in this paper is to provide a brief and non-exhaustive primer on ideas well-known in AP, but less so in other sub-disciplines. We do so by introducing the classical AP problem and representation, and extensions that handle uncertainty and time through the Markov Decision Process formalism. Next, we survey state-of-the-art techniques and ideas for solving AP problems, focusing on their ability to exploit problem structure. Lastly, we cover subfields within AP for learning structure from unstructured inputs and learning to generalise to unseen scenarios and situations.