Learning Schemas for Robot Perception
Active from 2000 - 2007
Many AI researchers have claimed that perception and thought are mediated through large scale, compositional, competitive knowledge structures known variously as frames (Minsky), scripts (Schank), or schemata (Rumelhart). Traditional AI systems hand-engineer these structures in service of a particular task. This project seeks to show how a robot could learn such structures from raw sensor data, through interaction with its environment. The system uses a combination of self-organizing feature maps (SOMs), and a growing auto-associative memory to construct schemas, bottom-up, from raw sensor data.
Jefferson Provost Ph.D. Alumni jefferson provost [at] gmail com