
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…
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