  DCSP (Distributed Constraint Satisfaction Problem) has been a very important
research area in AI (Artificial Intelligence). There are many application
problems in distributed AI that can be formalized as DSCPs. With the increasing
complexity and problem size of the application problems in AI, the required
storage place in searching and the average searching time are increasing too.
Thus, to use a limited storage place efficiently in solving DCSP becomes a very
important problem, and it can help to reduce searching time as well. This paper
provides an efficient knowledge base management approach based on general usage
of hyper-resolution-rule in consistence algorithm. The approach minimizes the
increasing of the knowledge base by eliminate sufficient constraint and false
nogood. These eliminations do not change the completeness of the original
knowledge base increased. The proofs are given as well. The example shows that
this approach decrease both the new nogoods generated and the knowledge base
greatly. Thus it decreases the required storage place and simplify the
searching process.

Artificial Intelligence;Distributed, Parallel, and Cluster Computing