The Effect of Nogood Learning in Distributed Constraint Satisfaction
Katsutoshi Hirayama, Makoto Yokoo,
20th International Conference on Distributed Computing Systems (ICDCS-2000), 2000.

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 \textit{distributed breakout algorithm}, which was known to be the most efficient algorithm (in terms of cycles) for solving distributed constraint satisfaction problems.