A cost-sensitive machine learning method for the approach and recognize task

Abstract Tan M. and Schlimer, J.C., A cost-sensitive machine learning method for the approach and recognize task, Robotics and Autonomous Systems, 8 (1991) 31–45. The long-term goal of this reasearch is to build a robot which can survive in an unanticipated environment for a long time. Such a robot must learn which sensors to use, where to use them, and how to generate inexpensive and reliable effector procedures to accomplish its tasks. Current machine learning methods are inadequate because they ignore execution costs and are ill-prepared to handle prediction failures. This paper described the CSI, learning and control system that attempts to address this goal, CSL has been implemented in a real-world robot for the approach and recognize taks.

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