The Community for Technology Leaders
RSS Icon
Issue No.03 - Third Quarter (2012 vol.5)
pp: 274-284
S. Ozgur Oguz , Koc University, Istanbul
Ayse Kucukyilmaz , Koc University, Istanbul
Tevfik Metin Sezgin , Koc University, Istanbul
Cagatay Basdogan , Koc University, Istanbul
An active research goal for human-computer interaction is to allow humans to communicate with computers in an intuitive and natural fashion, especially in real-life interaction scenarios. One approach that has been advocated to achieve this has been to build computer systems with human-like qualities and capabilities. In this paper, we present insight on how human-computer interaction can be enriched by employing the computers with behavioral patterns that naturally appear in human-human negotiation scenarios. For this purpose, we introduce a two-party negotiation game specifically built for studying the effectiveness of haptic and audio-visual cues in conveying negotiation related behaviors. The game is centered around a real-time continuous two-party negotiation scenario based on the existing game-theory and negotiation literature. During the game, humans are confronted with a computer opponent, which can display different behaviors, such as concession, competition, and negotiation. Through a user study, we show that the behaviors that are associated with human negotiation can be incorporated into human-computer interaction, and the addition of haptic cues provides a statistically significant increase in the human-recognition accuracy of machine-displayed behaviors. In addition to aspects of conveying these negotiation-related behaviors, we also focus on and report game-theoretical aspects of the overall interaction experience. In particular, we show that, as reported in the game-theory literature, certain negotiation strategies such as tit-for-tat may generate maximum combined utility for the negotiating parties, providing an excellent balance between the energy spent by the user and the combined utility of the negotiating parties.
Haptic interfaces, Computers, Humans, Games, Computational modeling, Robots, Force, haptic negotiation., Human factors, experimentation, haptic I/O, haptic user interfaces, haptic guidance, dynamic systems and control, multimodal systems, virtual environment modeling, performance
S. Ozgur Oguz, Ayse Kucukyilmaz, Tevfik Metin Sezgin, Cagatay Basdogan, "Supporting Negotiation Behavior with Haptics-Enabled Human-Computer Interfaces", IEEE Transactions on Haptics, vol.5, no. 3, pp. 274-284, Third Quarter 2012, doi:10.1109/TOH.2012.37
[1] L. Rosenberg, "Virtual Fixtures: Perceptual Tools for Telerobotic Manipulation," Proc. IEEE Virtual Reality Ann. Int'l Symp., pp. 76-82, 1993.
[2] S. Payandeh and Z. Stanisic, "On Application of Virtual Fixtures as an Aid for Telemanipulation and Training," Proc. 10th Symp. Haptic Interfaces on Virtual Environment and Teleoperator Systems, pp. 18-23, 2002.
[3] J. Abbott and A. Okamura, "Virtual Fixture Architectures for Telemanipulation," Proc. IEEE Int'l Conf. Robotics and Automation, vol. 2, pp. 2798-2805, 2003.
[4] C. Basdogan, A. Kiraz, I. Bukusoglu, A. Varol, and S. Doğanay, "Haptic Guidance for Improved Task Performance in Steering Microparticles with Optical Tweezers," Optics Express, vol. 15, no. 18, pp. 11616-11621, 2007.
[5] J. Huegel and M. O'Malley, "Progressive Haptic and Visual Guidance for Training in a Virtual Dynamic Task," Proc. IEEE Haptics Symp., pp. 343-350, 2010.
[6] B. Forsyth and K. Maclean, "Predictive Haptic Guidance: Intelligent User Assistance for the Control of Dynamic Tasks," IEEE Trans. Visualization and Computer Graphics, vol. 12, no. 1, pp. 103-113, Jan./Feb. 2006.
[7] J. Lee and S. Choi, "Effects of Haptic Guidance and Disturbance on Motor Learning: Potential Advantage of Haptic Disturbance," Proc. IEEE Haptics Symp., pp. 335-342, 2010.
[8] K.B. Reed and M.A. Peshkin, "Physical Collaboration of Human-Human and Human-Robot Teams," IEEE Trans. Haptics, vol. 1, no. 2, pp. 108-120, July-Dec. 2008.
[9] N. Stefanov, A. Peer, and M. Buss, "Role Determination in Human-Human Interaction," Proc. the World Haptics: Third Joint EuroHaptics Conf. and Symp. Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 51-56, 2009.
[10] S. Oguz, A. Kucukyilmaz, T. Sezgin, and C. Basdogan, "Haptic Negotiation and Role Exchange for Collaboration in Virtual Environments," Proc. IEEE Haptics Symp., pp. 371-378, 2010.
[11] A. Kucukyilmaz, T. Sezgin, and C. Basdogan, "Conveying Intentions Through Haptics in Human-Computer Collaboration," Proc. IEEE World Haptics Conf,. pp. 421-426, 2011.
[12] P. Evrard and A. Kheddar, "Homotopy Switching Model for Dyad Haptic Interaction in Physical Collaborative Tasks," Proc. EuroHaptics Conf., pp. 45-50, 2009.
[13] M. Lawitzky, A. Mörtl, and S. Hirche, "Load Sharing in Human-Robot Cooperative Manipulation," Proc. IEEE Int'l Symp. Robot and Human Interactive Comm., pp. 185-191, 2010.
[14] T. Wojtara, M. Uchihara, H. Murayama, S. Shimoda, S. Sakai, H. Fujimoto, and H. Kimura, "Human-Robot Collaboration in Precise Positioning of a Three-Dimensional Object," Automatica, vol. 45, pp. 333-342, 2009.
[15] R. Groten, D. Feth, A. Peer, and M. Buss, "Shared Decision Making in a Collaborative Task with Reciprocal Haptic Feedback - An Efficiency-Analysis," Proc. IEEE Int'l Conf. Robotics and Automation, pp. 1834-1839, 2010.
[16] G. Shell, Bargaining for Advantage: Negotiation Strategies for Reasonable People. Penguin Books, 1999.
[17] A. Byde, M. Yearworth, K.-Y. Chen, C. Bartolini, and N. Vulkan, "Autona: A System for Automated Multiple 1-1 Negotiation," Proc. Fourth ACM Conf. Electronic Commerce, pp. 198-199, 2003.
[18] R.M. Coehoorn and N.R. Jennings, "Learning an Opponent's Preferences to Make Effective Multi-Issue Negotiation Trade-Offs," Proc. Int'l Conf. Entertainment Computing, pp. 59-68, 2004.
[19] K. Hindriks and D. Tykhonov, "Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning," Proc. Int'l Conf. Autonomous Agents and Multiagent Systems, pp. 331-338, 2008.
[20] R. Lin, S. Kraus, J. Wilkenfeld, and J. Barry, "Negotiating with Bounded Rational Agents in Environments with Incomplete Information Using an Automated Agent," Artificial Intelligence, vol. 172, nos. 6/7, pp. 823-851, 2008.
[21] D. Traum, S.C. Marsella, J. Gratch, J. Lee, and A. Hartholt, "Multi-Party, Multi-Issue, Multi-Strategy Negotiation for Multi-Modal Virtual Agents," Proc. Int'l Conf. Intelligent Virtual Agents, pp. 117-130, 2008.
[22] I. Erev and A.E. Roth, "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," Am. Economic Rev., vol. 88, no. 4, pp. 848-81, 1998.
[23] R.D. McKelvey and T.R. Palfrey, "An Experimental Study of the Centipede Game," Econometrica, vol. 60, no. 4, pp. 803-836, 1992.
[24] S. Saha, A. Biswas, and S. Sen, "Modeling Opponent Decision in Repeated One-Shot Negotiations," Proc. Int'l Conf. Autonomous Agents and Multiagent Systems, pp. 397-403, 2005.
[25] Y. Oshrat, R. Lin, and S. Kraus, "Facing the Challenge of Human-Agent Negotiations via Effective General Opponent Modeling," Proc. Int'l Conf. Autonomous Agents and Multiagent Systems, pp. 377-384, 2009.
[26] R. Axelrod and W. Hamilton, "The Evolution of Cooperation," Science, vol. 211, no. 4489, pp. 1390-1396, 1981.
[27] N.A. Johnson and R.B. Cooper, "Power and Concession in Computer-Mediated Negotiations: An Examination of First Offers," MIS Quarterly, vol. 33, no. 1, pp. 147-170, 2009.
[28] R.H. Guttman and P. Maes, "Cooperative vs. Competitive Multi-Agent Negotiations in Retail Electronic Commerce," Proc. Int'l Workshop Cooperative Information Agents, vol. 1435, pp. 135-147, 1998.
[29] C. Basdogan, C. Ho, M.A. Srinivasan, and M. Slater, "An Experimental Study on the Role of Touch in Shared Virtual Environments," ACM Trans. Computer-Human Interaction, vol. 7, no. 4, pp. 443-460, 2000.
43 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool