Code Improvements for Model Elimination Based Reasoning Systems
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Author(s)
Hagen, Richard
Goodwin, Scott
Sattar, Abdul
Year published
2004
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We have been investigating ways in which the performance of model elimination based systems can be improved and in this paper we present some of our results. Firstly, we have investigated code improvements based on local and global analysis of the internal knowledge base used by the theorem prover. Secondly, we have looked into the use of a n lists to represent ancestor goal information to see if this gives a performance boost over the traditional two list approach. This n list representation might be thought of as a simple hash table. Thirdly, we conducted initial investigations into the effect of rule body literal ...
View more >We have been investigating ways in which the performance of model elimination based systems can be improved and in this paper we present some of our results. Firstly, we have investigated code improvements based on local and global analysis of the internal knowledge base used by the theorem prover. Secondly, we have looked into the use of a n lists to represent ancestor goal information to see if this gives a performance boost over the traditional two list approach. This n list representation might be thought of as a simple hash table. Thirdly, we conducted initial investigations into the effect of rule body literal ordering on performance. The results for the code improvements show them to be worthwhile, producing gains in some example problems. Using the n list representation gave mixed results: for some examples it improved execution speed, in others it degraded it. A rule body literal ordering that placed instantiated goals (including hypotheses) early in the bodies of rules showed an improvement in execution time.
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View more >We have been investigating ways in which the performance of model elimination based systems can be improved and in this paper we present some of our results. Firstly, we have investigated code improvements based on local and global analysis of the internal knowledge base used by the theorem prover. Secondly, we have looked into the use of a n lists to represent ancestor goal information to see if this gives a performance boost over the traditional two list approach. This n list representation might be thought of as a simple hash table. Thirdly, we conducted initial investigations into the effect of rule body literal ordering on performance. The results for the code improvements show them to be worthwhile, producing gains in some example problems. Using the n list representation gave mixed results: for some examples it improved execution speed, in others it degraded it. A rule body literal ordering that placed instantiated goals (including hypotheses) early in the bodies of rules showed an improvement in execution time.
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Conference Title
Computer Science 2004
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Copyright Statement
© 2004 Australian Computer Society Inc. The attached file is reproduced here in accordance with the copyright policy of the publisher. Use hypertext link to access the publisher's website.
Subject
History and Archaeology