By Muhammad Safdar MCS [E-Section]

 There are times in life when you are faced with challenging decisions to make. You have rules to follow and general guidelines to adhere to, but even those don’t make the path to be taken black and white. The inverse is also true. In life, sometimes all we are given is a problem and we must find a solution to this problems.  The same is true, and even more so, for expert systems. At times, all they are given is a statement or conclusion, and are expected to tell the user why this end point in some form of reasoning is fact or fiction. Other times, they are given conditions and are expected to make the right decision based on them.  Here begins The Differences Between Backward Chaining and Forward Chaining.

 Backward chaining (or backward reasoning) is an inference method that can be described (in lay terms) as working backward from the goal(s). It is used in automated theorem proves, inference engines, proof assistants and other artificial intelligence applications.  It is a form of reverse engineering, which is very applicable in situations where there are so many rules that could be applied to a single problem, the system could be there all day sifting through rules before it gets anywhere.  This method is also called goal-driven.

 Backward chaining is used for interrogative applications (finding items that fulfil certain criteria).  one commercial example of a backward chaining application might be finding which insurance policies are covered by a particular reinsurance contract.

 Forward chaining is one of the two main methods of reasoning when using an inference engine and can be described logically as repeated application of modus ponens. Forward chaining is a popular implementation strategy for expert systems, business and production rule systems.  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 (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.  This method is also called data-driven.

 Event driven systems are a common application of forward chaining rule engines.  One example of a forward chaining application might be a telecoms plan provisioning engine (typically used for administering mobile phone plans).

 The exploration of knowledge has different mechanisms in forward and backward chaining. Backward chaining is more focused and tries to avoid exploring unnecessary paths of reasoning. Forward chaining, on the other hand is like an exhaustive search.