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The Rete algorithm provides a generalized logical description of an implementation of functionality responsible for matching data tuples (" facts ") against productions (" rules ") in a pattern-matching production system ( a category of rule engine ).
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Rete and algorithm
The Jess rules engine utilizes the Rete algorithm, and can be utilized to create:
The Rete algorithm ( or, rarely or ) is an efficient pattern matching algorithm for implementing production rule systems.
The Rete algorithm was designed by Dr Charles L. Forgy of Carnegie Mellon University, first published in a working paper in 1974, and later elaborated in his 1979 Ph. D. thesis and a 1982 paper ( see References ).
The Rete algorithm provides the basis for a more efficient implementation.
The Rete algorithm is designed to sacrifice memory for increased speed.
In very large expert systems, however, the original Rete algorithm tends to run into memory consumption problems.
The Rete algorithm exhibits the following major characteristics:
The Rete algorithm is widely used to implement matching functionality within pattern-matching engines that exploit a match-resolve-act cycle to support forward chaining and inferencing.
Conflict resolution is not defined as part of the Rete algorithm, but is used alongside the algorithm.
As for conflict resolution, the firing of activated production instances is not a feature of the Rete algorithm.
The Rete algorithm does not mandate any specific approach to indexing the working memory.
For a more detailed and complete description of the Rete algorithm, see chapter 2 of Production Matching for Large Learning Systems by Robert Doorenbos ( see link below ).
Although not defined by the Rete algorithm, some engines provide extended functionality to support greater control of truth maintenance.
The Rete algorithm does not define any mechanism to define and handle these logical truth dependencies automatically.
The Rete algorithm does not define any approach to justification.
Some engines provide built-in justification systems in conjunction with their implementation of the Rete algorithm.
This article does not provide an exhaustive description of every possible variation or extension of the Rete algorithm.
The Rete algorithm is orientated to scenarios where forward chaining and " inferencing " is used to calculate new facts from existing facts, or to filter and discard facts in order to arrive at some conclusion.
In the 1980s, Dr Charles Forgy developed a successor to the Rete algorithm named Rete II.
Unlike the original Rete ( which is public domain ) this algorithm was not disclosed.
Rete II can be characterized by two areas of improvement ; specific optimizations relating to the general performance of the Rete network ( including the use of hashed memories in order to increase performance with larger sets of data ), and the inclusion of a backward chaining algorithm tailored to run on top of the Rete network.
Rete and provides
* The diagram provides a logical view of the Rete.
Rete and description
* Production Matching for Large Learning Systems – R Doorenbos Detailed and accessible description of Rete, also describes a variant named Rete / UL, optimised for large systems ( PDF )
Rete and implementation
Allen Newell's research group in artificial intelligence had been working on production systems for some time, but Forgy's implementation, based on his Rete algorithm, was especially efficient, sufficiently so that it was possible to scale up to larger problems involving hundreds or thousands of rules.
Rete and responsible
Only on 1 June 2000 the two main divisions, service and infrastructure, were separated and two different independent companies were created: Trenitalia, responsible for transport service, and Rete Ferroviaria Italiana, responsible for the management of the rail infrastructure.
Rete and for
In vertebrates, they are called a Rete mirabile, originally the name of an organ in fish gills for absorbing oxygen from the water.
Rete has become the basis for many popular rule engines and expert system shells, including Tibco Business Events, CLIPS, Jess, Drools, JRules, OPSJ, Blaze Advisor, BizTalk Rules Engine and Soar.
The word ' Rete ' is Latin for ' net ' or ' comb '.
However, by implementing additional beta node types, it is possible for Rete networks to perform quantifications.
Another example concerns additional time-stamping facilities provided by many engines for each WME entering a Rete network, and the use of these time-stamps in conjunction with conflict resolution strategies.
Several optimisations for Rete have been identified and described in academic literature.
Rete II claims better performance for more complex problems ( even orders of magnitude ), and is officially implemented in CLIPS / R2.
Backward chaining alone can account for the most extreme changes in benchmarks relating to Rete vs. Rete II.
* Charles Forgy, " Rete: A Fast Algorithm for the Many Pattern / Many Object Pattern Match Problem ", Artificial Intelligence, 19, pp 17 – 37, 1982
The Rete Mirabile allows for an increase in muscle temperature in regions where this network of vein and arteries is found.
Dr Charles L. Forgy ( born December 12, 1949 in Texas ) is a computer scientist, known for developing the Rete algorithm used in his OPS5 and other production system languages used to build expert systems.
Even though Forgy did not work directly on the DEC XCON AI problem of configuring computers for DEC in the late 70's and early 80's, the Rete algorithm was later incorporated into the system for more speed.
Rete and matching
The Rete algorithm, developed by Charles Forgy, is an example of such a matching algorithm ; it was used in the OPS series of production system languages.
* Rete algorithm, an efficient pattern matching algorithm for implementing production rule systems
Rete and data
Rete networks act as a type of relational query processor, performing projections, selections and joins conditionally on arbitrary numbers of data tuples.
For example, engines may provide specialised support within the Rete network in order to apply pattern-matching rule processing to specific data types and sources such as programmatic objects, XML data or relational data tables.
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