research-article

Crowd‐Sourcing Real‐World Human‐Robot Dialogue and Teamwork through Online Multiplayer Games

Authors:

Sonia Chernova,

Cynthia BreazealAuthors Info & Claims

AI Magazine, Volume 32, Issue 4

Pages 100 - 111

https://doi.org/10.1609/aimag.v32i4.2380

Published: 01 December 2011 Publication History

Abstract

We present an innovative approach for large‐scale data collection in human‐robot interaction research through the use of online multi‐player games. By casting a robotic task as a collaborative game, we gather thousands of examples of human‐human interactions online, and then leverage this corpus of action and dialogue data to create contextually relevant social and task‐oriented behaviors for human‐robot interaction in the real world. We demonstrate our work in a collaborative search and retrieval task requiring dialogue, action synchronization, and action sequencing between the human and robot partners. A user study performed at the Boston Museum of Science shows that the autonomous robot exhibits many of the same patterns of behavior that were observed in the online data set and survey results rate the robot similarly to human partners in several critical measures.

References

[1]

Aamodt, A., and Plaza, E. 1994. Case‐Based Reasoning; Foundational Issues, Methodological Variations, and System Approaches. AI Communications 7(1): 39–59.

[2]

Bernstein, M., Little, G., Miller, R., Hartmann, B., Ackerman, M., Karger, D., Crowell, D.; and Panovich, K. 2010. Soylent: A Word Processor with a Crowd Inside. In Proceedings of the 23nd Annual ACM Symposium on User Interface Software and Technology, 313–322. New York: Association for Computing Machinery.

Digital Library

[3]

Chernova, S., and Veloso, M. 2009. Interactive Policy Learning through Confdence‐Based Autonomy. Journal of Artificial Inelligence Research 34(1): 1–25.

Digital Library

[4]

Crick, C., Osentoski, S., Jay, G.; and Jenkins, O. C. 2011. Human and Robot Perception in Large‐Scale Learning from Demonstration. In Proceedings of the 6th International Conference on Human‐Robot Interaction, 339–346. New York: Association for Computing Machinery.

Digital Library

[5]

Haklay, M., and Weber, P. 2008. OpenStreetMap: User‐Generated Street Maps. IEEE Pervasive Computing 7(4): 12–18.

Digital Library

[6]

Knox, W., and Stone, P. 2009. Interactively Shaping Agents via Human Reinforcement: The TAMER Framework. In Proceedings of the Fifth International Conference on Knowledge Capture, 9–16. New York: Association for Computing Machinery.

Digital Library

[7]

Kollar, T., Tellex, S., Roy, D.; and Roy, N. 2010. Grounding Verbs of Motion in Natural Language Commands to Robots. In Proceedings of the 12th International Symposium on Experimental Robotics. Berlin: Springer.

[8]

Kolodner, J. 1993. Case‐Based Reasoning. San Francisco: Morgan Kaufmann Publishers Inc.

[9]

Micarelli, A., Panzieri, S.; and Sansonetti, G. 2007. Case‐Based Reasoning in Robot Indoor Navigation. In Proceedings of the Seventh International Conference on Case‐Based Reasoning: Case‐Based Reasoning Research and Development, 284–298. Berlin: Springer‐Verlag.

[10]

Orkin, J., and Roy, D. 2007. The Restaurant Game: Learning Social Behavior and Language from Thousands of Players Online. Journal of Game Development 3(1): 39–60.

[11]

Orkin, J., and Roy, D. 2009. Automatic Learning and Generation of Social Behavior from Collective Human Gameplay. In Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems, 385–392. Richland, SC: International Foundation for Autonomous Agenets and Multiagent Systems.

[12]

Ros, R., Arcos, J. L., Lopez de Mantaras, R.; and Veloso, M. 2009. A Case‐Based Approach for Coordinated Action Selection in Robot Soccer. Artificial Intelligence 173(9–10): 1014–1039.

Digital Library

[13]

Singh, P., Lin, T., Mueller, E., Lim, G., Perkins, T.; and Li Zhu, W. 2010. Open Mind Common Sense: Knowledge Acquisition from the General Public. On the Move to Meaningful Internet Systems 2002: CoopIS, DOA, and ODBASE 1223–1237. Berlin: Springer.

[14]

Sorokin, A., and Forsyth, D. 2008. Utility Data Annotation with Amazon's Mechanical Turk. In Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 1–8. Piscataway, NJ: Institute of Electrical and Electronics Engineers.

[15]

Sorokin, A., Berenson, D., Srinivasa, S.; and Hebert, M. 2010. People Helping Robots Helping People: Crowd‐sourcing for Grasping Novel Objects. In Proceedings of the 19th IEEE/RSJ International Conference on Intelligent Robots and Systems, 2117–2122. Piscataway, NJ: Institute of Electrical and Electronics Engineers.

[16]

Thomaz, A., and Breazeal, C. 2007. Asymmetric Interpretations of Positive and Negative Human Feedback for a Social Learning Agent. In Proceedings of the 16th IEEE International Symposium on Robot and Human‐Interactive Communication, 720–725. Institute of Electrical and Electronics Engineers.

[17]

von Ahn, L., and Dabbish, L. 2008. Designing Games with a Purpose. Communications of the ACM 51(8): 58–67.

Digital Library

Cited By

Lyu A(2023)Research on Online Game Design Based on Artificial Intelligence AlgorithmProceedings of the 6th International Conference on Information Technologies and Electrical Engineering10.1145/3640115.3640195(501-504)Online publication date: 3-Nov-2023
https://dl.acm.org/doi/10.1145/3640115.3640195

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© 2011 The Authors. AI Magazine published by John Wiley & Sons Ltd on behalf of Association for the Advancement of Artificial Intelligence.

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American Association for Artificial Intelligence

United States

Publication History

Published: 01 December 2011

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Cited By

Lyu A(2023)Research on Online Game Design Based on Artificial Intelligence AlgorithmProceedings of the 6th International Conference on Information Technologies and Electrical Engineering10.1145/3640115.3640195(501-504)Online publication date: 3-Nov-2023
https://dl.acm.org/doi/10.1145/3640115.3640195

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