Forward chaining starts with the available data and uses inference rules to extract more data (from an end user, for example) until a goal is reached. An inference engine using forward chaining searches the inference rules until it finds one where the antecedent (If clause) is known to be true. When such a rule is found, the engine can conclude, or infer, the consequent (Then clause), resulting in the addition of new information to its data.[1]
Inference engines will iterate through this process until a goal is reached.
Suppose that the goal is to conclude the color of a pet named Fritz, given that he croaks and eats flies, and that the rule base contains the following four rules:
IfX croaks and X eats flies - ThenX is a frog
IfX chirps and X sings - ThenX is a canary
IfX is a frog - ThenX is green
IfX is a canary - ThenX is blue
Let us illustrate forward chaining by following the pattern of a computer as it evaluates the rules.
Assume the following facts:
Fritz croaks
Fritz eats flies
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 backward chaining, which works the other way around.
In the derivation, the rules are used in the opposite order as compared to backward chaining.
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 data-driven, in contrast to goal-drivenbackward chaining inference. The forward chaining approach is often employed by expert systems, such as CLIPS.
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.[2][3]
Forward chaining is a powerful reasoning strategy with numerous applications in AI and related fields. Some of the prominent applications include:
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.
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.
Intelligent Tutoring Systems: Educational software often employs forward chaining to adapt to students’ progress and provide customized learning paths and feedback.
Decision Support Systems: Forward chaining is utilized in business and management decision support systems to analyze data and recommend actions or strategies.
Natural Language Processing: In natural language processing, forward chaining can be applied to resolve ambiguities in language and extract useful information from text.[4]
^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.
