Researchers at the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory (CSAIL) say they have developed a system that combines existing robotic control programs to enable multiagent systems to collaborate in much more complex ways.
The system factors in uncertainty and automatically plans around it. For small collaborative tasks, the system can guarantee that its combination of programs will yield the best possible results.
The researchers currently are testing the system in a simulation of a warehousing application, in which teams of robots would be required to retrieve arbitrary objects from indeterminate locations.
"Robots are on networks that are imperfect, so it takes some amount of time to get messages to other robots, and maybe they can't communicate in certain situations around obstacles," says CSAIL's Christopher Amato.
The system takes three inputs, one of which is a set of low-level control algorithms that may govern agents' behaviors collectively or individually. The system also has a set of statistics about those programs' execution in a particular environment, as well as a scheme for valuing different outcomes.
"The interesting thing about this paper is that they take these very complex tools and kind of decrease the resolution," says University of Southern California professor Nora Ayanian.
From MIT News
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