S&AS: FND: Norm Processing for Autonomous Social Systems

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• 批准号: 1723963
• 负责人: Matthias Scheutz
• 金额: $ 60万
• 依托单位: Tufts University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1723963&HistoricalAwards=false
• 关键词: S& FND; Norm; Processing; Autonomous

As intelligent systems continue to become more autonomous, it is important to imbue such systems with the types of social and moral norms that are deeply ingrained in human cognition and behavior. Failing to abide by these norms typically causes social reactions from humans, from blame and reprimands, in simple cases, all the way to full-fledged legal consequences. Given the fundamental social expectations humans have of each other, it is very likely that they will extend and apply those to artificial agents as well. Hence, intelligent autonomous systems will need to be endowed with computational mechanisms that ensure their ethical behavior.This project will develop explicit norm representations and planning algorithms for norm-conforming behavior that will address shortcomings of previous approaches, enabling explicit, temporally complex norm representations in stochastic worlds that allow for systematic handling of norm conflicts in ways that maximize the norms an agent can obey at any moment in time. In particular, explicit norm specifications will allow for generalization across unobserved states. In addition, it is important for systems interacting with humans to make decisions that are transparent. To that end, the approach will enable the generation of justifications in cases where an autonomous system is forced to violate some norms because not all can be satisfied simultaneously. The approach and algorithms developed will be demonstrated on a fully autonomous cleaning robot with predefined norms in different scenarios, where conflicting norms are applicable and the robot must determine which norms to follow and which to suspend.

随着智能系统继续变得越来越自治，重要的是要将此类系统与人类认知和行为深深根深蒂固的社会和道德规范类型融为一体。 不遵守这些规范通常会导致人类的社会反应，责备和谴责，在简单的情况下，一直到成熟的法律后果。 鉴于人类彼此之间的基本社会期望，他们很可能也会扩展并将其应用于人造代理。 因此，智能自主系统将需要赋予确保其道德行为的计算机制。该项目将开发明确的规范表示和计划算法，以实现规范符合规范的行为，这些行为将解决以前方法的缺点，以实现明显的，具有暂时性复杂的规范代表性的随机阶段，从而可以在任何规范中进行系统的范围来遵守规范，以实现任何规范的范围，以实现任何规范的范围。 特别是，显式规范规格将允许在未观察到的状态之间进行概括。 此外，对于与人类互动的系统，做出透明的决策很重要。 为此，在自主系统被迫违反某些规范的情况下，该方法将能够产生一系列理由，因为并非所有人都可以同时满足。 开发的方法和算法将在具有预定义规范的完全自主清洁机器人上进行证明，在不同的情况下，相互冲突的规范是适用的，并且机器人必须确定遵循哪些规范以及暂停哪些规范。

项目成果

Norms, Rewards, and the Intentional Stance: Comparing Machine Learning Approaches to Ethical Training

规范、奖励和意图立场：机器学习方法与道德培训的比较

• DOI: 10.1145/3278721.3278774
• 发表时间: 2018
• 期刊: and Society
• 作者: Kasenberg, Daniel;Arnold, Thomas;Scheutz, Matthias
• 通讯作者: Scheutz, Matthias

A Touching Connection: How Observing Robotic Touch Can Affect Human Trust in a Robot

感人的联系：观察机器人触摸如何影响人类对机器人的信任

• DOI: 10.1007/s12369-020-00729-7
• 发表时间: 2021
• 期刊: International Journal of Social Robotics
• 影响因子: 4.7
• 作者: Law, Theresa;Malle, Bertram F.;Scheutz, Matthias
• 通讯作者: Scheutz, Matthias

Generating Explanations for Temporal Logic Planner Decisions

• DOI: 10.1609/icaps.v30i1.6740
• 发表时间: 2020-06
• 作者: Daniel Kasenberg;Ravenna Thielstrom;matthias. scheutz

Explaining in Time: Meeting Interactive Standards of Explanation for Robotic Systems

• DOI: 10.1145/3457183
• 发表时间: 2021-07-01
• 期刊: ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION
• 影响因子: 5.1
• 作者: Arnold, Thomas;Kasenberg, Daniel;Scheutz, Matthias
• 通讯作者: Scheutz, Matthias

Quasi-dilemmas for artificial moral agents

人工道德主体的准困境

• DOI: 10.13180/icres.2018.20-21.08.012
• 发表时间: 2018
• 期刊: International Conference on Robot Ethics and Standards
• 作者: Daniel, Kasenberg