Forward chaining

<h2 id="example">Example</h2>
Suppose that the goal is to conclude the color of a pet named Fritz, given that he croaks and eats flies, and that the <a href="/facts/Rule_base/xmlMNr7j">rule base</a> contains the following four rules:

<ol><li>If X croaks and X eats flies - Then X is a frog</li>
<li>If X chirps and X sings - Then X is a canary</li>
<li>If X is a frog - Then X is green</li>
<li>If X is a canary - Then X is blue</li></ol>
Let us illustrate forward chaining by following the pattern of a computer as it evaluates the rules.
Assume the following facts:

<ul><li>Fritz croaks</li>
<li>Fritz eats flies</li></ul>
With forward reasoning, the inference engine can derive that Fritz is green in a series of steps:
1. Since the base facts indicate that "Fritz croaks" and "Fritz eats flies", the antecedent of rule #1 is satisfied by substituting Fritz for X, and the inference engine concludes:

 Fritz is a frog

2. The antecedent of rule #3 is then satisfied by substituting Fritz for X, and the inference engine concludes:

 Fritz is green

The name "forward chaining" comes from the fact that the inference engine starts with the data and reasons its way to the answer,
as opposed to <a href="/facts/Backward_chaining/St5dtGYq">backward chaining</a>, which works the other way around.
In the derivation, the rules are used in the opposite order as compared to <a href="/facts/Backward_chaining/St5dtGYq">backward chaining</a>.
In this example, rules #2 and #4 were not used in determining that Fritz is green.
Because the data determines which rules are selected and used, this method is called <a href="/facts/Data-driven_science/AHYUo0Rn">data-driven</a>, in contrast to <a href="/facts/Goal-oriented/lG4kP4EF">goal-driven</a> <a href="/facts/Backward_chaining/St5dtGYq">backward chaining</a> inference. The forward chaining approach is often employed by <a href="/facts/Expert_system/CUCzSEaX">expert systems</a>, such as <a href="/facts/CLIPS/5Sek388z">CLIPS</a>.
One of the advantages of forward-chaining over backward-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.<a class="footnote-ref" id="fnref:2" href="#fn:2">2</a><a class="footnote-ref" id="fnref:3" href="#fn:3">3</a>

<h2 id="applications">Applications</h2>
Forward chaining is a powerful reasoning strategy with numerous applications in AI and related fields. Some of the prominent applications include:

<ul><li>Expert Systems: Expert systems are AI systems that mimic the decision-making abilities of human experts in a specific domain. They rely on forward chaining to apply expert knowledge to solve problems and make recommendations.</li>
<li>Diagnosis and Troubleshooting: Forward chaining is extensively used in medical diagnosis and troubleshooting systems, where the input symptoms and test results are used to determine potential causes and treatments.</li>
<li>Intelligent Tutoring Systems: Educational software often employs forward chaining to adapt to students’ progress and provide customized learning paths and feedback.</li>
<li>Decision Support Systems: Forward chaining is utilized in business and management decision support systems to analyze data and recommend actions or strategies.</li>
<li>Natural Language Processing: In natural language processing, forward chaining can be applied to resolve ambiguities in language and extract useful information from text.<a class="footnote-ref" id="fnref:4" href="#fn:4">4</a></li></ul>
<h2 id="see-also">See also</h2>
<ul><li><a href="/facts/Backward_chaining/St5dtGYq">Backward chaining</a></li>
<li><a href="/facts/Constraint_Handling_Rules/5lLG1Gra">Constraint Handling Rules</a></li>
<li><a href="/facts/Opportunistic_reasoning/NgXmxWIy">Opportunistic reasoning</a></li>
<li><a href="/facts/Rete_algorithm/Uw85WXLm">Rete algorithm</a></li></ul>

<h2 id="external-links">External links</h2>
<ul><li><a href="https://web.archive.org/web/20130907161909/http://answers.semanticweb.com/questions/3304/forward-vs-backward-chaining">Forward vs. Backward Chaining Explained at SemanticWeb.com</a></li></ul>

<h2 id="references">References</h2>

<ol>
<li id="fn:1">Feigenbaum, Edward (1988). The Rise of the Expert Company. Times Books. p. 318. ISBN 0-8129-1731-6. <a href="0-8129-1731-6" target="_blank">0-8129-1731-6</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></li>
<li id="fn:2">Hayes-Roth, Frederick; Donald Waterman; Douglas Lenat (1983). Building Expert Systems. Addison-Wesley. ISBN 0-201-10686-8. <a href="0-201-10686-8" target="_blank">0-201-10686-8</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></li>
<li id="fn:3">Kaczor, Krzystof; Szymon Bobek; Grzegorz J. Nalepa (2010-12-05). "Overview of Expert System Shells" (PDF). geist.agh.edu.pl/. Krakow, Poland: Institute of Automatics: AGH University of Science and Technology, Poland. Retrieved 5 December 2013. <a href="https://home.agh.edu.pl/~ligeza/wiki/lib/exe/fetch.php?media=ke:ruleinfalg.pdf" target="_blank">https://home.agh.edu.pl/~ligeza/wiki/lib/exe/fetch.php?media=ke:ruleinfalg.pdf</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></li>
<li id="fn:4">"Applications of Forward Chaining". www.doubtly.in. Retrieved 2023-11-02. <a href="https://www.doubtly.in/forward-chaining/" target="_blank">https://www.doubtly.in/forward-chaining/</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></li>
</ol>

Forward chaining open-in-new

Forward chaining