Active adaptation in human-agent collaborative interaction
When a human user interacts with an adaptive agent to achieve human-agent collaboration, active adaptation is considered to be one of the critical characteristics of the agent. In order to investigate the principal features of active adaptation, we developed a human-agent collaborative experimental environment called WAITER (waiter agent interactive training experimental restaurant) and conducted two types of experiments, a Wizard of OZ (WOZ) agent experiment and an autonomous agent experiment. The objective of these experiments is to observe how human users perceive the agents and change their instructions when interacting with adaptive agents. The results indicate that humans can recognize changes in the agent’s actions and change their instruction methods accordingly. This implies that active adaptation of the agents may encourage the adaptation of the human users and help to build an adaptation loop between them. The experimental results also suggest that active adaptation may play an important role in a human-agent collaborative task.
KeywordsActive adaptation Human-agent collaboration Wizard of OZ agent Autonomous agent Mutual adaptation
- Bishop, C. M. (2007). Pattern recognition and machine learning (information science and statistics). Springer.Google Scholar
- Goldman, C. V., Rosenschein, J. S., & Rosenschein, J. S. (1996). Incremental and mutual adaptation in multiagent systems. Tech. rep., Institute of Computer Science, The Hebrew University.Google Scholar
- Komatsu, T., Utsunomiya, A., Suzuki, K., Ueda, K., Hiraki, K., & Oka, N. (2005). Experiments toward a mutual adaptive speech interface that adopts the cognitive features humans use for communication and induces and exploits users’ adaptation. International Journal of Human-Computer Interaction, 18(3), 243–268.CrossRefGoogle Scholar
- Maes, P. (1994). Modeling adaptive autonomous agents. Artificial Life, 1(1–2), 135–162.Google Scholar
- MathWorks Inc. (2010). Creating a graphical user interface. Website: http://www.mathworks.com/demos/matlab/creating-a-graphical-user-interface-matlab-video-tutorial.html. Accessed 1 August 2010.
- Wooldridge, M. (2000). Reasoning about rational agents. The MIT.Google Scholar
- Xu, Y., Ohmoto, Y., Okada, S., Ueda, K., Komatsu, T., Okadome, T., et al. (2010). Formation conditions of mutual adaptation in human-agent collaborative interaction. Applied Intelligence. doi: 10.1007/s10489-010-0255-y.
- Xu, Y., Ohmoto, Y., Ueda, K., Komatsu, T., Okadome, T., Kamei, K., et al. (2008). Two-layered communicative protocol model in a cooperative directional guidance task. In The 7th international workshop on social intelligence design. Puerto Rico.Google Scholar
- Xu, Y., Ohmoto, Y., Ueda, K., Komatsu, T., Okadome, T., Kamei, K., et al. (2009a). A platform system for developing a collaborative mutually adaptive agent. In International conference on industrial, engineering & other applications of applied intelligent systems (IEA/AIE 2009). Lecture notes in computer science: Next-generation applied intelligence (Vol. 5579, pp. 576–585). Berlin: Springer.Google Scholar
- Yamada, S., & Kakusho, K. (2003). IDEA: Interaction design for adaptation (in Japanese). Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 15(2), 185.Google Scholar
- Yamada, S., & Yamaguchi, T. (2004). Training AIBO like a dog. In The 13th international workshop on robot and human interactive communication (ROMAN-2004) (pp. 431–436). Kurashiki, Japan.Google Scholar
- Yamada, S., & Yamaguchi, T. (2005). Mutual adaptation of mind mappings between a human and a life-like agent. Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 17(3), 289–297.Google Scholar
- Yamasaki, N., & Anzai, Y. (1995). Active interface for human-robot interaction. In IEEE International Conference on Robotics and Automation, 3, 3103–3109.Google Scholar