Learn More
Using computational intelligence, our ultimate goal is to self-regulate systems composed of humans, machines and robots. Self-regulation is important for the control of mixed organizations and systems. An overview of self-regulation for organizations and systems, characterized by our solution of the tradeoffs between Fourier pairs of Gaussian distributions(More)
One of the great challenges of putting humanoid robots into space is developing cognitive capabilities for the robots with an interface that allows human astronauts to collaborate with the robots as naturally and efficiently as they would with other astronauts. In this joint effort with NASA and the entire Robonaut team we are integrating natural language(More)
Game theory's popularity continues to increase in a variety of disciplines such as economics, biology, political science, computer science, electrical engineering, business, law, public policy, and many others. The focus of this symposium was to bring together the community working on applied computational game theory motivated by any of these domains. This(More)
We are developing the physics of interdependent uncertainty relations to efficiently and effectively control interdependence in autonomous hybrid teams (i.e., arbitrary combinations of humans, robots and machines), which cannot be done presently. Uncertainty is created in states of interdependence between social objects: at one extreme, interdependence(More)
— This paper proposes a path-planning approach to enable a team of unmanned aerial vehicles (UAVs) to efficiently conduct surveillance of sensitive areas. The proposed approach, termed PARCOV (Planner for Autonomous Risk-sensitive Coverage), seeks to maximize the area covered by the sensors mounted on each UAV while maintaining high sensor data quality and(More)