Team Plan Recognition: A Review of the State of the Art

TL;DR

This paper reviews the current state of team plan recognition, focusing on logic-based methods, discussing challenges, and highlighting recent approaches to improve AI understanding of human team activities.

Contribution

It provides a comprehensive survey of logic-based team plan recognition methods, emphasizing recent developments and identifying future research directions.

Findings

Logic-based methods are effective for team plan recognition.

Recent approaches improve accuracy in understanding team actions.

The survey highlights gaps and challenges in current methods.

Abstract

There is an increasing need to develop artificial intelligence systems that assist groups of humans working on coordinated tasks. These systems must recognize and understand the plans and relationships between actions for a team of humans working toward a common objective. This article reviews the literature on team plan recognition and surveys the most recent logic-based approaches for implementing it. First, we provide some background knowledge, including a general definition of plan recognition in a team setting and a discussion of implementation challenges. Next, we explain our reasoning for focusing on logic-based methods. Finally, we survey recent approaches from two primary classes of logic-based methods (plan library-based and domain theory-based). We aim to bring more attention to this sparse but vital topic and inspire new directions for implementing team plan recognition.