Effect of nogood learning in distributed constraint satisfaction

We present resolvent-based learning as a new nogood learning method for a distributed constraint satisfaction algorithm. This method is based on a look-back technique in constraint satisfaction algorithms and can efficiently make effective nogoods. We combine the method with the asynchronous weak-commitment search algorithm (AWC) and evaluate the performance of the resultant algorithm on distributed 3-coloring problems and distributed 3SAT problems. As a result, we found that the resolvent-based learning works well compared to previous learning methods for distributed constraint satisfaction algorithms. We also found that the AWC with the resolvent-based learning is able to find a solution with fewer cycles than the distributed breakout algorithm, which was known to be the most efficient algorithm (in terms of cycles) for solving distributed constraint satisfaction problems.