平山 勝敏， 横尾 真
人工知能学会誌, Vol.15, No.2, pp.355--361,
We present resolvent-based learning <\cite> 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.