Forward chaining is one of the two main methods of reasoning when using inference rules (in artificial intelligence). The other is backward chaining. ... Garry Kasparov playing against Deep Blue, the first machine to win a chess game against a reigning world champion. ... Backward chaining is one of the two main methods of reasoning when using inference rules. ...

Forward chaining starts with the available data and uses inference rules to extract more data (from an end user for example) until an optimal goal is reached. An inference engine using forward chaining searches the inference rules until it finds one where the If clause is known to be true. When found it can conclude, or infer, the Then clause, resulting in the addition of new information to its dataset. For other uses, see Data (disambiguation). ... In logic, especially in mathematical logic, a rule of inference is a scheme for constructing valid inferences. ... In mathematics, the term optimization, or mathematical programming, refers to the study of problems in which one seeks to minimize or maximize a real function by systematically choosing the values of real or integer variables from within an allowed set. ... An objective or goal is a personal or organizational desired end point in development. ... An inference engine tries to derive answers from a knowledge base. ... The ASCII codes for the word Wikipedia represented in binary, the numeral system most commonly used for encoding computer information. ...

Inference engines will often cycle through this process until an optimal goal is reached.

For example, suppose that the goal is to conclude the color of my pet Fritz, given that he croaks and eats flies, and that the rulebase contains the following two rules:

1. If Fritz croaks and eats flies - Then Fritz is a frog
2. If Fritz is a frog - Then Fritz is green

The given rule (that Fritz croaks and eats flies) would first be added to the knowledgebase, as the rulebase is searched for a consequent that matches its antecedent; This is true of the first Rule, so the conclusion (that Fritz is a Frog) is also added to the knowledgebase, and the rulebase is again searched. This time, the second rules' antecedent matches our consequent, so we add to our knowledgebase the new conclusion (that Fritz is green). Nothing more can be inferred from this information, but we have now accomplished our goal of determining the color of Fritz. Distribution of frogs (in black) Suborders Archaeobatrachia Mesobatrachia Neobatrachia - List of Anuran families The frogness babe is an amphibian in the order Anura (meaning tail-less from Greek an-, without + oura, tail), formerly referred to as Salientia (Latin saltare, to jump). ... A knowledge base is a special kind of database for knowledge management. ... A consequent is the second half of a hypothetical proposition. ... An antecedent is the first half of a hypothetical proposition. ... A knowledge base is a special kind of database for knowledge management. ... An antecedent is the first half of a hypothetical proposition. ... A consequent is the second half of a hypothetical proposition. ... A knowledge base is a special kind of database for knowledge management. ...

Forward-chaining inference is often called data driven — in contrast to backward-chaining inference, which is referred to as goal driven reasoning. The top-down approach of forward chaining is commonly used in expert systems, such as CLIPS. One of the advantages of forward-chaining over backwards-chaining is that the reception of new data can trigger new inferences, which makes the engine better suited to dynamic situations in which conditions are likely to change. Eager evaluation or strict evaluation is the evaluation strategy in most traditional programming languages. ... This article does not cite any references or sources. ... CLIPS is a public-domain software tool for building expert systems. ...

See Also

• Backward chaining