Legal Personality for AI

Turner, Jacob

doi:10.1007/978-3-319-96235-1_5

Cited by 6 publications

(1 citation statement)

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“…The sources of normative information could also be expanded, allowing for the inference of norms from how agents interact with objects and other agents in the environment. While some ethicists and legislators have argued that robots should own themselves (Turner 2019), much work remains before robots can understand the very concept of ownership.…”

Section: Discussionmentioning

confidence: 99%

That’s Mine! Learning Ownership Relations and Norms for Robots

Tan

Brawer

Scassellati

2019

AAAI

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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) perceptbased 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.

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Section: Discussionmentioning

confidence: 99%