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Maria Pittou, Stavros Tripakis.  2016.  Multi-view consistency for infinitary regular languages. International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation, {SAMOS} 2016, Agios Konstantinos, Samos Island, Greece, July 17-21, 2016. :148–155.
Linh Phan, Kaikai Liu.  2017.  Multi-Tenant Network Acceleration Scheme for OpenStack. IEEE IEEE Smart City Innovation (SCI).
Kirill Trapeznikov, Venkatesh Saligrama, David A. Castañón.  2012.  Multi-Stage Classifier Design. Proceedings of the 4th Asian Conference on Machine Learning, {ACML} 2012, Singapore, Singapore, November 4-6, 2012. 25:459–474.
A. Thudimilla, B. McMillin.  2017.  Multiple Security Domain Nondeducibility Air Traffic Surveillance Systems. 2017 IEEE 18th International Symposium on High Assurance Systems Engineering (HASE). :136-139.
Kanteti, U., McMillin, B.,.  2017.  Multiple Security Domain Model of a Vehicle in an Automated Vehicle System,. Proceedings of the Eleventh IFIP WG 11.10 International Conference on Critical Infrastructure Protection.
Hei, Xiali, Lin, Shan.  2014.  Multi-part File Encryption for Electronic Health Records Cloud. Proceedings of the 4th ACM MobiHoc Workshop on Pervasive Wireless Healthcare. :31–36.
H. Ding, D. A. Castanon.  2015.  Multi-object two-agent coordinated search. 2015 International Conference on Complex Systems Engineering (ICCSE). :1-6.
G. Greenwood, M. Podhradsky, J. Gallagher, E. Matson.  2015.  A Multi-Agent System for Autonomous Adaptive Control of a Flapping-Wing Micro Air Vehicle. 2015 IEEE Symposium Series on Computational Intelligence. :1073-1080.

Biomimetic flapping wing vehicles have attracted recent interest because of their numerous potential military and civilian applications. In this paper we describe the design of a multi-agent adaptive controller for such a vehicle. This controller is responsible for estimating the vehicle pose (position and orientation) and then generating four parameters needed for split-cycle control of wing movements to correct pose errors. These parameters are produced via a subsumption architecture rule base. The control strategy is fault tolerant. Using an online learning process an agent continuously monitors the vehicle's behavior and initiates diagnostics if the behavior has degraded. This agent can then autonomously adapt the rule base if necessary. Each rule base is constructed using a combination of extrinsic and intrinsic evolution. Details on the vehicle, the multi-agent system architecture, agent task scheduling, rule base design, and vehicle control are provided.

H. Ding, D. A. Castanon.  2017.  Multi-agent discrete search with limited visibility. 2017 Conference on Decision and Control).
Weerakkody, Sean, Sinopoli, Bruno.  2016.  A moving target approach for identifying malicious sensors in control systems. 54th Annual Allerton Conference on Communication, Control, and Computing. :1149–1156.
Erhan Baki Ermis, Venkatesh Saligrama, Pierre{-}Marc Jodoin, Janusz Konrad.  2008.  Motion segmentation and abnormal behavior detection via behavior clustering. Proceedings of the International Conference on Image Processing, {ICIP} 2008, October 12-15, 2008, San Diego, California, {USA}. :769–772.
J. Mike McHugh, Janusz Konrad, Venkatesh Saligrama, Pierre{-}Marc Jodoin, David A. Castañón.  2008.  Motion detection with false discovery rate control. Proceedings of the International Conference on Image Processing, {ICIP} 2008, October 12-15, 2008, San Diego, California, {USA}. :873–876.