A Computational Framework for Integrating Task Planning and Norm Aware Reasoning for Social Robots

Abstract

Autonomous robots are envisioned to increasingly become part of our lives in the house, restaurants, hospitals and offices. Additionally, self-driving cars will be soon appearing in city streets and highways and they will have to interact with cars driven by humans as well as other self-driving cars. In these settings the robots not only need to efficiently perform their tasks but also be able to interact with humans in socially appropriate ways. To accomplish this, robots must be able to reason not only on how to perform their tasks, but also incorporate societal values, social norms and legal rules so they can gain human acceptability and trust. Moreover, interactions with these robots will be long term. Long-term human interaction with robots as well as robot combined reasoning about both tasks and social norms generate multiple modeling and computational challenges. In this paper, we address one of the most important of these challenges, namely what is an appropriate and scalable computational framework that enables simultaneous task and normative reasoning. In particular, we report on our work on a novel computational framework, Modular Normative Markov Decision Processes (MNMDP) that integrates reasoning for domain tasks and normative reasoning for long-term autonomy. The MNMDP framework applies normative reasoning considering only the norms that are activated in appropriate contexts, rather than considering the full set of norms, thus significantly reducing computational complexity. The model modularity is also advantageous for long-term human-robot interaction. We present computational experiments that show significant computational improvements as compared with a base Normative Markov Decision Process (MDP) framework that includes the full set of norms.