Computing partial solutions to difficult AI problems

Roman V. Yampolskiy, University of Louisville


Is finding just a part of a solution easier than finding the full solution? For NP-Complete problems (which represent some of the hardest problems for AI to solve) it has been shown that finding a fraction of the bits in a satisfying assignment is as hard as finding the full solution. In this paper we look at a possibility of both computing and representing partial solutions to NP-complete problems, but instead of computing bits of the solution our approach relies on restricted specifications of the problem search space. We show that not only could partial solutions to NP-Complete problems be computed without computing the full solution, but also given an Oracle capable of providing pre-computed partial answer to an NP-complete problem an asymptotic simplification of problems is possible. Our main contribution is a standardized methodology for search space specification which could be used in many distributed computation project to better coordinate necessary computational efforts.