これは私のもの！ロボットの所有関係と規範の学習
That's Mine! Learning Ownership Relations and Norms for Robots
物体と所有者の確率的関係をグラフで表し、規範データベースと組み合わせて、ロボットが所有関係や規範を学習・推論できるシステムを提案した。
著者: Zhi-Xuan Tan, Jake Brawer, Brian Scassellati
分類: cs.AI, cs.RO
原文アブストラクト
The ability for autonomous agents to learn and conform to human norms is crucial for their safety and effectiveness in social environments. While recent work has led to frameworks for the representation and inference of simple social rules, research into norm learning remains at an exploratory stage. Here, we present a robotic system capable of representing, learning, and inferring ownership relations and norms. Ownership is represented as a graph of probabilistic relations between objects and their owners, along with a database of predicate-based norms that constrain the actions permissible on owned objects. To learn these norms and relations, our system integrates (i) a novel incremental norm learning algorithm capable of both one-shot learning and induction from specific examples, (ii) Bayesian inference of ownership relations in response to apparent rule violations, and (iii) percept-based prediction of an object's likely owners. Through a series of simulated and real-world experiments, we demonstrate the competence and flexibility of the system in performing object manipulation tasks that require a variety of norms to be followed, laying the groundwork for future research into the acquisition and application of social norms.
