@inproceedings{5f86389c161b405b99f11ef7cbf68dfa,
title = "Feature-based Joint Planning and Norm Learning in Collaborative Games",
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).",
keywords = "computational modeling, features, joint intentionality, norms, reinforcement learning, team reasoning",
author = "Ho, {Mark K.} and James MacGlashan and Amy Greenwald and Littman, {Michael L.} and Hilliard, {Elizabeth M.} and Carl Trimbach and Stephen Brawner and Tenenbaum, {Joshua B.} and Max Kleiman-Weiner and Austerweil, {Joseph L.}",
note = "Publisher Copyright: {\textcopyright} 2016 Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016. All rights reserved.; 38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016 ; Conference date: 10-08-2016 Through 13-08-2016",
year = "2016",
language = "English (US)",
series = "Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016",
publisher = "The Cognitive Science Society",
pages = "1158--1163",
editor = "Anna Papafragou and Daniel Grodner and Daniel Mirman and Trueswell, {John C.}",
booktitle = "Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016",
}