Teaching social behavior through human reinforcement for ad hoc teamwork-the star framework

Shani Alkoby, Avilash Rath, Peter Stone

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

As AI technology continues to develop, more and more agents will become capable of long term autonomy alongside people. Thus, a recent line of research has studied the problem of teaching autonomous agents the concept of ethics and human social norms. Most existing work considers the case of an individual agent attempting to learn a predefined set of rules. In reality however, social norms are not always pre-defined and are very difficult to represent algorithmically. Moreover, the basic idea behind the social norms concept is ensuring that one's actions do not negatively influence others' utilities, which is inherently a multiagent concept. Thus, here we investigate a way to teach agents, as a team, how to act according to human social norms. In this research, we introduce the star framework used to teach an ad hoc team of agents to act in accordance with human social norms. Using a hybrid team (agents and people), when taking an action considered to be socially unacceptable, the agents receive negative feedback from the human teammate(s) who has(have) an awareness of the team's norms. We view star as an important step towards teaching agents to act more consistently with respect to human morality.

Original languageEnglish
Title of host publication18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1773-1775
Number of pages3
ISBN (Electronic)9781510892002
StatePublished - 2019
Externally publishedYes
Event18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 - Montreal, Canada
Duration: 13 May 201917 May 2019
https://dl.acm.org/doi/proceedings/10.5555/3306127

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume3
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
Country/TerritoryCanada
CityMontreal
Period13/05/1917/05/19
Internet address

Bibliographical note

Publisher Copyright:
© 2019 International Foundation for Autonomous Agents and Multiagent Systems. All rights reserved.

Keywords

  • Ad hoc
  • Reinforcement learning
  • Social norms

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