Efficient Knowledge Base Management in DCSP

TL;DR

This paper presents an efficient knowledge base management method for DCSP that reduces storage and search time by eliminating unnecessary constraints and false nogoods without losing completeness.

Contribution

It introduces a hyper-resolution-rule-based approach to minimize knowledge base growth in DCSP solving, improving efficiency and scalability.

Findings

Reduces knowledge base size significantly

Decreases number of generated nogoods

Simplifies search process

Abstract

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.