Curious partner: An approach to realize common ground in human-autonomy collaboration

A dialog-based human-autonomy interaction ap- proach, called curious partner, is presented for a class of systems where the role of autonomy is to assist humans in decision making tasks. Even if the human and the autonomy share the environment and receive identical information, they may have inconsistencies in the representation of the environment due to difference in their perception and expert knowledge. The curious partner interaction framework is presented to resolve model-level differences between the human and the autonomy to establish common ground. The knowledge base of the autonomy is modeled using a Bayesian engine. The autonomy's dialog with the human acts as a feedback mechanism to resolve any differences either by suggesting maximally probable actions to human based on the state of its Bayesian model or by updating its model to achieve analogous world representation.

[1]  Su Wei,et al.  Learning Bayesian network parameters based on iterative learning control , 2011, 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet).

[2]  Mark E. Campbell,et al.  Variational Bayesian data fusion of multi-class discrete observations with applications to cooperative human-robot estimation , 2010, 2010 IEEE International Conference on Robotics and Automation.

[3]  Alexei Makarenko,et al.  Human-robot communication for collaborative decision making - A probabilistic approach , 2010, Robotics Auton. Syst..

[4]  Terrence Fong,et al.  The human-robot interaction operating system , 2006, HRI '06.

[5]  Terrence Fong,et al.  Collaboration, Dialogue, Human-Robot Interaction , 2001, ISRR.

[6]  Han-Pang Huang,et al.  Bayesian human intention estimator for exoskeleton system , 2013, 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[7]  Sara B. Kiesler,et al.  Eliciting information from people with a gendered humanoid robot , 2005, ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005..

[8]  Rajesh P. N. Rao,et al.  Dynamic Imitation in a Humanoid Robot through Nonparametric Probabilistic Inference , 2006, Robotics: Science and Systems.

[9]  Danica Kragic,et al.  Enhanced visual scene understanding through human-robot dialog , 2010, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Danica Kragic,et al.  Enhanced visual scene understanding through human-robot dialog , 2011, IROS 2011.

[11]  Bonnie M. Muir,et al.  Trust in automation. I: Theoretical issues in the study of trust and human intervention in automated systems , 1994 .

[12]  Jaime Valls Miró,et al.  Dynamic Bayesian Networks for Learning Interactions between Assistive Robotic Walker and Human Users , 2010, KI.

[13]  Mark E. Campbell,et al.  Scalable Bayesian human-robot cooperation in mobile sensor networks , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Stephen M. Rock,et al.  Dialogue-based human-robot interaction for space construction teams , 2002, Proceedings, IEEE Aerospace Conference.

[15]  Sara B. Kiesler,et al.  Fostering common ground in human-robot interaction , 2005, ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005..

[16]  Kolja Kühnlenz,et al.  A Multimodal Human-Robot-Dialog Applying Emotional Feedbacks , 2010, ICSR.

[17]  N. Moray,et al.  Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation. , 1996, Ergonomics.

[18]  Alois Knoll,et al.  Integrating Language, Vision and Action for Human Robot Dialog Systems , 2007, HCI.

[19]  Sven Wachsmuth,et al.  On Grounding Natural Kind Terms in Human-Robot Communication , 2013, KI - Künstliche Intelligenz.

[20]  Plamen J. Prodanov,et al.  Bayesian networks based multi-modality fusion for error handling in human-robot dialogues under noisy conditions , 2005, Speech Commun..

[21]  Young-Jun Son,et al.  An extended BDI model for human behaviors: Decision-making, learning, interactions, and applications , 2013, 2013 Winter Simulations Conference (WSC).

[22]  Changsong Liu,et al.  Collaborative Effort towards Common Ground in Situated Human-Robot Dialogue , 2014, 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[23]  Christopher R. Brown,et al.  Dynamic Bayes net approach to multimodal sensor fusion , 1997, Other Conferences.

[24]  Jun Zhang,et al.  A graph partitioning approach for Bayesian Network structure learning , 2014, CCC 2014.

[25]  Danica Kragic,et al.  Task-Based Robot Grasp Planning Using Probabilistic Inference , 2015, IEEE Transactions on Robotics.

[26]  Sung-Bae Cho,et al.  Mixed-Initiative Human–Robot Interaction Using Hierarchical Bayesian Networks , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[27]  Illah R. Nourbakhsh,et al.  Using a robot proxy to create common ground in exploration tasks , 2008, 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[28]  Stefan Kopp,et al.  Co-constructing Grounded Symbols—Feedback and Incremental Adaptation in Human–Agent Dialogue , 2013, KI - Künstliche Intelligenz.