KSL-93-07 + redirect page
Controlling Inference Using the Query Tree + Has identifier
Controlling Inference Using the Query Tree + Ksl tr id
Controlling Inference Using the Query Tree + Number
| Controlling Inference Using the Query Tree |
Bibtype
techreport
Has publishing details
January,1993
Has title
Controlling Inference Using the Query Tree
Has where published
KSL-93-07
Has year
1993
Title
Controlling Inference Using the Query Tree
Year
1993
Abstract
Controlling inference is a key to scaling … Controlling inference is a key to scaling up AI systems. This paper describes methods for controlling inference in Horn rule knowledge bases using a powerful tool, the {\em query tree}. The query tree is a finite structure that encodes all the possible derivations of a query. It shows which facts in the knowledge base may be used in a derivation of the query. Furthermore, it encodes all the possible sequences of rule applications and database lookups that can result in answers to the query. Consequently, it can be used to control search both by ignoring certain facts and by guiding the search of the problem solver to pursue only useful paths. The distinguishing characteristic of the query tree is that under certain conditions, it encodes {\em only} useful derivation paths and tells us {\em precisely} which facts can be used in a derivation of the query. We present experimental results showing that in practice, using the query tree to control search leads to significant savings. ntrol search leads to significant savings.
Author
Alon Y. Halevy and Yehoshua Sagiv +
Has author
Alon Y. Halevy and Yehoshua Sagiv +
Has identifier
Controlling Inference Using the Query Tree +
Institution
Knowledge Systems, AI Laboratory +
Ksl tr id
Controlling Inference Using the Query Tree +
Month
January +
Number
Controlling Inference Using the Query Tree +
Process note
NO +
Categories KSL Technical Report +, Publication +, Technical Report +
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