An algorithm for maintaining working memory consistency in multiple rule firing systems

Perraju, T.S.; Prasad, Bandreddi E.

doi:10.1016/s0169-023x(99)00038-5

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…It is 10-20 times faster than the standard lisp implementation distributed by Carnegie Mellon University. These methods break the interference cycle in several places, which make the selected compatible rule set have a relative small amount of rule instantiations (Perraju et al, 2000;Cheng et al, 2010;Kao, 2012;Wang et al, 2011). Therefore, it is necessary to get further promotion because of the small amount of parallelism.…”

Section: Related Workmentioning

confidence: 99%

“…The multiple rule firing method introduced in this paper is based on the idea of request access control of Perraju et al (2000), and only needs to process WMEs and negative tokens which are to be accessed directly without considering the concept of weak pattern (Perraju et al, 2000). Besides, this method uses data structure NegTokenRef to store the related information of the negative tokens in the master server which is different from directly accessing the alpha memories of the local Rete network to deal with the access requests of the negative tokens.…”

Section: Workflow Of Multiple Rule Firingmentioning

confidence: 99%

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A Novel Approach to Distributed Rule Matching and Multiple Firing Based on MapReduce

Dong

Cheng

Cao

et al. 2018

Journal of Database Management

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In order to solve the poor performance problem of massive rules reasoning, as well as the inconsistency problem of working memory in distributed rule matching, this article presents the formal definition of interference relations between rules, and proposes a novel approach to distributed rule matching and multiple firing based on MapReduce. This approach adopts the way of access request control to detect and exclude interference rules, then selects several rule instantiations to perform multiple firing and concurrent execution, thus reducing the number of inference cycles effectively. By detecting the interferences between rules, this method selects and executes compatible rule sets, and avoids the inconsistency problem of system working memory. In order to verify the validity of the authors' approach, this article developes a production system based on MapReduce, and applied this approach in the master server of a distributed production system. The experimental results show that their method can promote the performance of massive rules reasoning effectively.

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Section: Related Workmentioning

confidence: 99%

Section: Workflow Of Multiple Rule Firingmentioning

confidence: 99%