Exploring Improvisational Approaches to Social Knowledge Acquisition

Feng, D., Carstensdottir, E., El-Nasr, M. S. and Marsella, S. (2019) Exploring Improvisational Approaches to Social Knowledge Acquisition. In: 18th International Conference on Autonomous Agents and MultiAgent Systems, Montreal, QC, Canada, 13-17 May 2019, pp. 1060-1068. ISBN 9781450363099

190276.pdf - Accepted Version


Publisher's URL: https://dl.acm.org/citation.cfm?id=3306127.3331804


To build agents that can engage user in more open-ended social contexts, more and more attention has been focused on data-driven approaches to reduce the requirement of extensive, hand-authored behavioral content creation. However, one fundamental challenge of data-driven approaches, is acquiring human social interaction data with sufficient variety to capture more open-ended social interactions, as well as their coherency. Previous work has attempted to extract such social knowledge using crowdsourced narratives. This paper proposes an approach to acquire the knowledge of social interaction by integrating an improvisational theatre training technique into a crowdsourcing task aimed at collecting social narratives. The approach emphasizes theory of mind concepts, through an iterative prompting process about the mental states of characters in the narrative and paired writing, in order to encourage the authoring of diverse social interactions. To assess the effectiveness of integrating prompting and two-worker improvisation to the knowledge acquisition process, we systematically compare alternative ways to design the crowdsourcing task, including a) single worker vs. two workers authoring interaction between different characters in a given social context, and b) with or without prompts. Findings from 175 participants across two different social contexts show that the prompts and two-workers collaboration could significantly improve the diversity and the objective coherency of the narratives. The results presented in this paper can provide a rich set of diverse and coherent action sequences to inform the design of socially intelligent agents.

Item Type:Conference Proceedings
Additional Information:Funding for this research was provided by the National Science Foundation Cyber-Human Systems under Grant No. 1526275.
Glasgow Author(s) Enlighten ID:Marsella, Professor Stacy and Feng, Dr Dan
Authors: Feng, D., Carstensdottir, E., El-Nasr, M. S., and Marsella, S.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Copyright Holders:Copyright © 2019 International Foundation for Autonomous Agents and Multiagent Systems
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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