This document discusses using machine learning techniques for functional validation in grid computing environments. It proposes using PAC learning and Chernoff bounds theory to validate if a service provider can correctly perform a requested function based on sample test cases. If the service provider answers enough test cases correctly with high probability, the client can commit to using the service. It also discusses extending this approach to more complex validation scenarios using other machine learning models like zero-knowledge proofs and reinforcement learning.

1. Machine Learning Applications in Grid Computing George Cybenko, Guofei Jiang and Daniel Bilar Thayer School of Engineering Dartmouth College 22th Sept.,1999, 37th Allerton Conference Urbana-Champaign, Illinois Acknowledgements: This work was partially supported by AFOSR grants F49620-97-1-0382, NSF grant CCR-9813744 and DARPA contract F30602-98-2-0107.

14. Mobile Functional Validation Agent User Interface User Agent Interface Agent Computing Server A Mobile Agent Machine A Machine C, D, E, ... Create Send Jump A’s Service Correct Incorrect B’s Service Correct Incorrect C, D, E, ….. MA Interface Agent Computing Server B MA Machine B MA Correct Service