Knowledge engineering: the applied side of artificial intelligence
Proc. of a symposium on Computer culture: the scientific, intellectual, and social impact of the computer
ML92 Proceedings of the ninth international workshop on Machine learning
Explanation-Based Learning: An Alternative View
Machine Learning
Generative encoding for multiagent learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Falconet: force-feedback approach for learning from coaching and observation using natural and experiential training
Evolving policy geometry for scalable multiagent learning
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Learning tactical human behavior through observation of human performance
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The command and control of teams of autonomous vehicles provides a strong model of the control of cyber-physical systems in general. Using the definition of command and control for military systems, we can recognize the requirements for the operational control of many systems and see some of the problems that must be resolved. Among these problems are the need to distinguish between aberrant behaviors and optimal but quirky behaviors so that the human commander can determine if the behaviors conform to standards and align with mission goals. Similarly the commander must able to recognize when goals will not be met in order to reapportion assets available to the system. Robustness in the face of a highly variable environment can be met through machine learning, but must be done in a way that the tactics employed are recognizable as correct. Finally, because cyber-physical systems will involve decisions that must be made at great speed, we consider the use of the Rainbow framework for autonomics to provide rapid but robust command and control at pace.