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The Rete algorithm (ˈriːtiː , ˈreɪtiː , rarely ˈriːt , rɛˈteɪ ) is a pattern matching algorithm for implementing rule-based systems. The algorithm was developed to efficiently apply many rules or patterns to many objects, or facts, in a knowledge base. It is used to determine which of the system's rules should fire based on its data store, its facts. The Rete algorithm was designed by 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. A naive implementation of an expert system might check each rule against known facts in a knowledge base, firing that rule if necessary, then moving on to the next rule (and looping back to the first rule when finished). For even moderate sized rules and facts knowledge-bases, this naive approach performs far too slowly. The Rete algorithm provides the basis for a more efficient implementation. A Rete-based expert system builds a network of nodes, where each node (except the root) corresponds to a pattern occurring in the left-hand-side (the condition part) of a rule. The path from the root node to a leaf node defines a complete rule left-hand-side. Each node has a memory of facts which satisfy that pattern. This structure is essentially a generalized trie. As new facts are asserted or modified, they propagate along the network, causing nodes to be annotated when that fact matches that pattern. When a fact or combination of facts causes all of the patterns for a given rule to be satisfied, a leaf node is reached and the corresponding rule is triggered. Rete was first used as the core engine of the OPS5 production system language which was used to build early systems including R1 for Digital Equipment Corporation. Rete has become the basis for many popular rule engines and expert system shells, including CLIPS, Jess, Drools, IBM Operational Decision Management, BizTalk Rules Engine, and Soar. The word 'Rete' is Latin for 'net' or 'comb'. The same word is used in modern Italian to mean 'network'.