Abstract
This paper presents a Markov decision process based model for a socially assistive robot. Problem addressed by the model is one to one correspondence of a robot with a human where robot has to convince the human about completing certain tasks. In this regard, emotions of the human and those of the robot are incorporated in the model. Furthermore, emotion transition probabilities and probabilities of robot being able to successfully convince the human are also incorporated. The resulting model however involves large state space. Computational complexity involved in calculation of optimal decision policy from the proposed model is discussed. Consequently, a computational complexity reduction technique is proposed that uses decomposition of the tasks to be performed into sub groups. An online learning framework is also proposed to account for un-modeled parameters in the problem. Behavior of decision making optimal policy obtained from the proposed model has been demonstrated with the help of a simulation based case study.
Original language | English |
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Title of host publication | 2018 IEEE Conference on Decision and Control, CDC 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1198-1203 |
Number of pages | 6 |
ISBN (Electronic) | 9781538613955 |
DOIs | |
State | Published - 2 Jul 2018 |
Externally published | Yes |
Publication series
Name | Proceedings of the IEEE Conference on Decision and Control |
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Volume | 2018-December |
ISSN (Print) | 0743-1546 |
ISSN (Electronic) | 2576-2370 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization