Active Goal Recognition

TL;DR

This paper introduces active goal recognition (AGR), a novel approach where agents actively gather information to recognize goals efficiently, balancing information collection with task execution in cost-sensitive real-world scenarios.

Contribution

It formulates the AGR problem, combining goal recognition with active information gathering, and provides a model along with preliminary experimental results demonstrating its effectiveness.

Findings

Optimal AGR balances information gathering and task completion.

Preliminary results show improved efficiency in goal recognition.

Active strategies outperform passive monitoring in cost-sensitive environments.

Abstract

To coordinate with other systems, agents must be able to determine what the systems are currently doing and predict what they will be doing in the future---plan and goal recognition. There are many methods for plan and goal recognition, but they assume a passive observer that continually monitors the target system. Real-world domains, where information gathering has a cost (e.g., moving a camera or a robot, or time taken away from another task), will often require a more active observer. We propose to combine goal recognition with other observer tasks in order to obtain \emph{active goal recognition} (AGR). We discuss this problem and provide a model and preliminary experimental results for one form of this composite problem. As expected, the results show that optimal behavior in AGR problems balance information gathering with other actions (e.g., task completion) such as to achieve all tasks jointly and efficiently. We hope that our formulation opens the door for extensive further research on this interesting and realistic problem.