The objective of this work is to develop programs that can learn executable models of behavior as they observe physical social agents, where "executable" means that the models should run in simulation or on mobile robots; and "physical social agent" means an animal, robot or person that senses its environment, acts upon its environment, and normally operates as part of an interacting multi-agent group. The algorithms developed in this work will support a applications such as: the ability to observe social insect colonies, build models of their behavior, then execute and verify the models in simulation; the ability to observe motorized vehicle activity (e.g., cars on a highway), build models of their behavior, then use the models to evaluate highway designs in simulation; and the ability to train mobile robot teams by providing video examples of team behavior to be emulated. To these ends, the PI will leverage research in behavioral ecology, computer vision (especially activity recognition), and behavior-based robot control, having observed that the representations utilized in each of these areas are strikingly similar. The PI will investigate how natural, large-scale systems organize themselves and, in tandem, he will study how artificial robot teams can be programmed to cooperate effectively; the motivation for pursuing both lines of research at once is that the PI expects each to inform the other. The results of this project will advance our understanding of how intelligent, distributed multi-agent systems can be organized for effective performance.
Broader Impacts: The techniques developed in this work will profoundly influence research in several disciplines outside computer science, including ethology, ecology, and animal behavior. The software developed for learning and executing models of social behavior will be made available over the Internet to members of the research community. The PI will also develop new computer science courses whose topics are intertwined with this research, including one on Intelligent Robotics and Perception, another on Autonomous Multi-Robot Systems, and a third on Principles of Physical MultiAgent Systems.
