A Computational Framework for Integrating Task Planning and Norm Aware Reasoning for Social Robots
A Computational Framework for Integrating Task Planning and Norm Aware Reasoning for Social Robots
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DOI：
10.1109/roman.2018.8525577
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发表时间：
2018-08
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影响因子：
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通讯作者：
Vigneshram Krishnamoorthy;Wenhao Luo;M. Lewis;K. Sycara
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作者：
Vigneshram Krishnamoorthy;Wenhao Luo;M. Lewis;K. Sycara
自主机器人有望越来越多地成为我们生活的一部分，在家庭，餐馆，医院和办公室。此外，自动驾驶汽车将很快出现在城市街道和高速公路上，它们将不得不与人类驾驶的汽车以及其他自动驾驶汽车进行交互。在这些环境中，机器人不仅需要有效地执行任务，而且还能够以社会适当的方式与人类互动。为了实现这一目标，机器人不仅要能够推理如何执行任务，还要能够融入社会价值观、社会规范和法律的规则，以便获得人类的接受和信任。此外，与这些机器人的互动将是长期的。人类与机器人的长期交互以及机器人对任务和社会规范的综合推理产生了多种建模和计算挑战。在本文中，我们解决这些挑战中最重要的一个，即什么是一个适当的和可扩展的计算框架，使同时任务和规范推理。特别是，我们报告了我们的工作，一种新的计算框架，模块化规范马尔可夫决策过程（MNMDP），集成了推理领域的任务和规范推理的长期自治。MNMDP框架应用规范推理，只考虑在适当的上下文中激活的规范，而不是考虑全部规范，从而显着降低计算复杂性。模型模块化也有利于长期的人机交互。我们目前的计算实验表明，显着的计算改进相比，一个基本的规范马尔可夫决策过程（MDP）的框架，包括全套规范。
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.