TY - GEN
T1 - Coordination in human-robot teams using mental modeling and plan recognition
AU - Talamadupula, Kartik
AU - Briggs, Gordon
AU - Chakraborti, Tathagata
AU - Scheutz, Matthias
AU - Kambhampati, Subbarao
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/31
Y1 - 2014/10/31
N2 - Beliefs play an important role in human-robot teaming scenarios, where the robots must reason about other agents' intentions and beliefs in order to inform their own plan generation process, and to successfully coordinate plans with the other agents. In this paper, we cast the evolving and complex structure of beliefs, and inference over them, as a planning and plan recognition problem. We use agent beliefs and intentions modeled in terms of predicates in order to create an automated planning problem instance, which is then used along with a known and complete domain model in order to predict the plan of the agent whose beliefs are being modeled. Information extracted from this predicted plan is used to inform the planning process of the modeling agent, to enable coordination. We also look at an extension of this problem to a plan recognition problem. We conclude by presenting an evaluation of our technique through a case study implemented on a real robot.
AB - Beliefs play an important role in human-robot teaming scenarios, where the robots must reason about other agents' intentions and beliefs in order to inform their own plan generation process, and to successfully coordinate plans with the other agents. In this paper, we cast the evolving and complex structure of beliefs, and inference over them, as a planning and plan recognition problem. We use agent beliefs and intentions modeled in terms of predicates in order to create an automated planning problem instance, which is then used along with a known and complete domain model in order to predict the plan of the agent whose beliefs are being modeled. Information extracted from this predicted plan is used to inform the planning process of the modeling agent, to enable coordination. We also look at an extension of this problem to a plan recognition problem. We conclude by presenting an evaluation of our technique through a case study implemented on a real robot.
UR - http://www.scopus.com/inward/record.url?scp=84911464399&partnerID=8YFLogxK
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U2 - 10.1109/IROS.2014.6942970
DO - 10.1109/IROS.2014.6942970
M3 - Conference contribution
AN - SCOPUS:84911464399
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 2957
EP - 2962
BT - IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
Y2 - 14 September 2014 through 18 September 2014
ER -