Integrating Grammatical Inference into Robotic Planning

Jane Chandlee, Jie Fu, Konstantinos Karydis, Cesar Koirala, Jeffrey Heinz, Herbert Tanner
Proceedings of the Eleventh International Conference on Grammatical Inference, PMLR 21:69-83, 2012.

Abstract

This paper presents a method for the control synthesis of robotic systems in an unknown, dynamic, and adversarial environments. We (1) incorporate a grammatical inference module that identifies the governing dynamics of the adversarial environment and (2) utilize game theory to compute a motion plan for a system given a task specification. The framework is flexible and modular since different games can be formulated for different system objectives and different grammatical inference algorithms can be utilized depending on the abstract nature of the dynamic environment.

Cite this Paper

BibTeX
@InProceedings{pmlr-v21-chandlee12a, title = {Integrating Grammatical Inference into Robotic Planning}, author = {Chandlee, Jane and Fu, Jie and Karydis, Konstantinos and Koirala, Cesar and Heinz, Jeffrey and Tanner, Herbert}, booktitle = {Proceedings of the Eleventh International Conference on Grammatical Inference}, pages = {69--83}, year = {2012}, editor = {Heinz, Jeffrey and Higuera, Colin and Oates, Tim}, volume = {21}, series = {Proceedings of Machine Learning Research}, address = {University of Maryland, College Park, MD, USA}, month = {05--08 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v21/chandlee12a/chandlee12a.pdf}, url = {https://proceedings.mlr.press/v21/chandlee12a.html}, abstract = {This paper presents a method for the control synthesis of robotic systems in an unknown, dynamic, and adversarial environments. We (1) incorporate a grammatical inference module that identifies the governing dynamics of the adversarial environment and (2) utilize game theory to compute a motion plan for a system given a task specification. The framework is flexible and modular since different games can be formulated for different system objectives and different grammatical inference algorithms can be utilized depending on the abstract nature of the dynamic environment.} }
Endnote
%0 Conference Paper %T Integrating Grammatical Inference into Robotic Planning %A Jane Chandlee %A Jie Fu %A Konstantinos Karydis %A Cesar Koirala %A Jeffrey Heinz %A Herbert Tanner %B Proceedings of the Eleventh International Conference on Grammatical Inference %C Proceedings of Machine Learning Research %D 2012 %E Jeffrey Heinz %E Colin Higuera %E Tim Oates %F pmlr-v21-chandlee12a %I PMLR %P 69--83 %U https://proceedings.mlr.press/v21/chandlee12a.html %V 21 %X This paper presents a method for the control synthesis of robotic systems in an unknown, dynamic, and adversarial environments. We (1) incorporate a grammatical inference module that identifies the governing dynamics of the adversarial environment and (2) utilize game theory to compute a motion plan for a system given a task specification. The framework is flexible and modular since different games can be formulated for different system objectives and different grammatical inference algorithms can be utilized depending on the abstract nature of the dynamic environment.
RIS
TY - CPAPER TI - Integrating Grammatical Inference into Robotic Planning AU - Jane Chandlee AU - Jie Fu AU - Konstantinos Karydis AU - Cesar Koirala AU - Jeffrey Heinz AU - Herbert Tanner BT - Proceedings of the Eleventh International Conference on Grammatical Inference DA - 2012/08/16 ED - Jeffrey Heinz ED - Colin Higuera ED - Tim Oates ID - pmlr-v21-chandlee12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 21 SP - 69 EP - 83 L1 - http://proceedings.mlr.press/v21/chandlee12a/chandlee12a.pdf UR - https://proceedings.mlr.press/v21/chandlee12a.html AB - This paper presents a method for the control synthesis of robotic systems in an unknown, dynamic, and adversarial environments. We (1) incorporate a grammatical inference module that identifies the governing dynamics of the adversarial environment and (2) utilize game theory to compute a motion plan for a system given a task specification. The framework is flexible and modular since different games can be formulated for different system objectives and different grammatical inference algorithms can be utilized depending on the abstract nature of the dynamic environment. ER -
APA
Chandlee, J., Fu, J., Karydis, K., Koirala, C., Heinz, J. & Tanner, H.. (2012). Integrating Grammatical Inference into Robotic Planning. Proceedings of the Eleventh International Conference on Grammatical Inference, in Proceedings of Machine Learning Research 21:69-83 Available from https://proceedings.mlr.press/v21/chandlee12a.html.

Related Material