What if Pinocchio Were a Reinforcement Learning Agent: A Normative End-to-End Pipeline

TL;DR

This paper introduces a pipeline for developing norm-compliant, context-aware reinforcement learning agents supervised by argumentation-based advisors, addressing societal rule adherence in AI systems.

Contribution

It presents a novel hybrid model combining RL with normative argumentation advisors and a new algorithm for extracting normative arguments automatically.

Findings

Empirical evaluation of each pipeline component.

Demonstration of norm avoidance mitigation strategies.

Analysis of the pipeline's effectiveness in normative compliance.

Abstract

In the past decade, artificial intelligence (AI) has developed quickly. With this rapid progression came the need for systems capable of complying with the rules and norms of our society so that they can be successfully and safely integrated into our daily lives. Inspired by the story of Pinocchio in ``Le avventure di Pinocchio - Storia di un burattino'', this thesis proposes a pipeline that addresses the problem of developing norm compliant and context-aware agents. Building on the AJAR, Jiminy, and NGRL architectures, the work introduces \pino, a hybrid model in which reinforcement learning agents are supervised by argumentation-based normative advisors. In order to make this pipeline operational, this thesis also presents a novel algorithm for automatically extracting the arguments and relationships that underlie the advisors' decisions. Finally, this thesis investigates the phenomenon of \textit{norm avoidance}, providing a definition and a mitigation strategy within the context of reinforcement learning agents. Each component of the pipeline is empirically evaluated. The thesis concludes with a discussion of related work, current limitations, and directions for future research.