- Live-fire military training exercises are expensive in terms of personnel, ordnance, fuel, and environmental damage. Virtual training technologies and the Navy and US Marine Corps’ family of tactical decision making simulations (TDSs) provide training tools for trainees to plan and execute operational plans in a force-on-force environment and through after action review, gain feedback about the effectiveness of their planning and decision making. However, TDSs require the participation and coordination of instructors (experts) and many human players because the capability for automatically controlling a realistic, competent opposing force is relatively non-existent. Furthermore, tactical decision making simulations are not designed to address more strategic decision making. Thus, the inability of current technology to provide a competitive, realistic, opposing force compromises the goal of inexpensive, anytime, anywhere training, especially for strategic decision making.
- The goal of this project is to develop a computational approach to developing effective training systems for virtual simulation environments. Our proposed solution is to develop intelligent, autonomous controllers that drive the behavior of each boat in the virtual training environment. To increase the system’s efficiency we provide a mechanism for creating such controllers, from the demonstration of a navigation expert, using a simple programming interface. In addition, our approach deals with two significant and related challenges: the realism of behavior exhibited by the automated boats and their real-time response to changes in the environment.
- Siming Liu, Sushil J. Louis, and Monica Nicolescu, "Comparing Heuristic Search Methods for Finding Effective Group Behaviors in RTS Game", in 2013 IEEE Congress on Evolutionary Computation (CEC), pages 1371-1378, 2013. [PDF] [Link]
- Siming Liu, Sushil J. Louis, and Monica Nicolescu, "Using Cigar for Finding Effective Group Behaviors in RTS Game", in 2013 IEEE Conference on Computational Intelligence in Games (CIG), pages 1-8. IEEE, 2013. [PDF] [Link]
- Monica Nicolescu, Ryan Leigh, Adam Olenderski, Sushil Louis, Sergiu Dascalu, Chris Miles, Juan Quiroz, Ryan Aleson, "A Training Simulation System with Realistic Autonomous Ship Control", Computational Intelligence, Special Issue on Artificial Intelligence Methods for Ambient Intelligence, vol. 23, no. 4, pages 497-516, November 2007. [PDF]
- Adam Olenderski, Monica Nicolescu, Sushil Louis, "A Behavior-Based Architecture for Realistic Autonomous Ship Control", in Proceedings, IEEE Symposium on Computational Intelligence and Games (CIG06), Reno, NV, USA, May 22-24, 2006. [PDF]
- Ryan Leigh, Tony Morelli, Sushil Louis, Monica Nicolescu, Chris Miles, "Finding Attack Strategies for Predator Swarms Using Genetic Algorithms", in Proceedings, IEEE Congress on Evolutionary Computation, Edinburgh, SCOTLAND, September 2-5, 2005. [PDF]
- Adam Olenderski, Monica Nicolescu, Sushil Louis, "Robot Learning by Demonstration Using Forward Models of Schema-Based Behaviors", in Proceedings, International Conference on Informatics in Control, Automation and Robotics, Barcelona, SPAIN, September 14-17, 2005. [PDF]
- Virtual At Sea Training, Office of Naval Research, Co-PI: Monica Nicolescu (PI: Sushil Louis, Co-PI: Sergiu Dascalu), Amount: $416,584, October 1, 2005 - September 30, 2008.
- Adaptive Intelligent Swarms for VAST-COVE: Virtual Training of Conning Officers in Ship Self Defense Against Small Boats, Office of Naval Research, Co-PI (PI: Sushil Louis), Amount: $220,241, July 1, 2004 - June 30, 2006.