Becoming increasingly reactive

  • Authors:
  • Tom M. Mitchell

  • Affiliations:
  • School of Computer Science, Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
  • Year:
  • 1990

Quantified Score

Hi-index 0.00

Visualization

Abstract

We describe a robot control architecture which combines a stimulus-response subsystem for rapid reaction, with a search-based planner for handling unanticipated situations. The robot agent continually chooses which action it is to perform, using the stimulus-response subsystem when possible, and falling back on the planning subsystem when necessary. Whenever it is forced to plan, it applies an explanation-based learning mechanism to formulate a new stimulus-response rule to cover this new situation and others similar to it. With experience, the agent becomes increasingly reactive as its learning component acquires new stimulus-response rules that eliminate the need for planning in similar subsequent situations. This Theo-Agent architecture is described, and results are presented demonstrating its ability to reduce routine reaction time for a simple mobile robot from minutes to under a second.