Feature-based Joint Planning and Norm Learning in Collaborative Games

Mark K. Ho, James MacGlashan, Amy Greenwald, Michael L. Littman, Elizabeth M. Hilliard, Carl Trimbach, Stephen Brawner, Joshua B. Tenenbaum, Max Kleiman-Weiner, Joseph L. Austerweil

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

6 Scopus citations

Abstract

People often use norms to coordinate behavior and accomplish shared goals. But how do people learn and represent norms? Here, we formalize the process by which collaborating individuals (1) reason about group plans during interaction, and (2) use task features to abstractly represent norms. In Experiment 1, we test the assumptions of our model in a gridworld that requires coordination and contrast it with a “best response” model. In Experiment 2, we use our model to test whether group members' joint planning relies more on state features independent of other agents (landmark-based features) or state features determined by the configuration of agents (agent-relative features).

Original languageEnglish
Title of host publicationProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016
EditorsAnna Papafragou, Daniel Grodner, Daniel Mirman, John C. Trueswell
Pages1158-1163
Number of pages6
ISBN (Electronic)9780991196739
StatePublished - 2016
Event38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016 - Philadelphia, United States
Duration: 10 Aug 201613 Aug 2016

Publication series

NameProceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016

Conference

Conference38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016
Country/TerritoryUnited States
CityPhiladelphia
Period10/08/1613/08/16

Keywords

  • computational modeling
  • features
  • joint intentionality
  • norms
  • reinforcement learning
  • team reasoning

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