What is control strategy in AI explain forward chaining and backward chaining strategies?

What is forward chaining?

Forward chaining in AI is a form of reasoning while using an inference engine. It is also called forward deduction or forward reasoning. The inference engine is a part of the expert system that applies logical rules on the knowledge base for the purpose of deducing new data. It analyzes and interprets the facts in the knowledge base (a structured collection of facts regarding the system’s domain) for the purpose of finding answers. 

It is a way of reasoning used in artificial intelligence which begins with atomic sentences in the knowledge base and proceeds to apply inference rules to derive new information until an endpoint or a goal is achieved.

A forward-chaining algorithm will begin with facts that are known. It will proceed to trigger all the inference rules whose premises are satisfied and then add the new data derived from them to the known facts, repeating the process till the goal is achieved or the problem is solved.

The inference engine essentially goes through the inference rules till it finds one for which the  antecedent (If clause) is known to be true. After the inference engine finds such a rule, it can deduce or conclude the the consequent (Then clause). This results in the addition of new data, The inference engine keeps iterating through this process till it reaches the goal.

The forward reasoning method is employed in planning, monitoring, controlling, and interpreting applications.

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What are the properties of forward chaining?

Here are the properties of forward chaining or forward reasoning:

• It employs a down-up approach, moving from the bottom to the top.
• It uses known facts to start from the initial state and reach the goal state (or a conclusion).
• Forward reasoning is a data-driven approach in the sense that it uses data to reach the goal.
• It is usually used in production rule systems and expert systems like the DENDRAL expert system which uses it to establish the molecular and chemical structure of substances.

Why do we use forward chaining?

The forward chaining strategy is used when the final outcome is not known, but there are facts available about the domain in which the expert system functions. It uses the dacts and data available in the knowledge base to reach the goal state or the final outcome. 

The process of forward chaining is generally used for planning, monitoring, controlling, and interpreting applications. It is an implementation strategy that is very widely used in expert systems, business and production rule systems. 

Expert systems

Expert systems make use of forward chaining to allow a program to do things that only an expert can do, like identifying insects or providing advice regarding purchasing stocks by making inferences and conclusions on the basis of the facts that are available. Due to the fact that the process is based on a set of rules, the deduction is rather straightforward.

Production Rule Systems

Production rule systems also use an inference engine for forward chaining. These systems are built with a set of rules regarding behaviors and procedures. These rules serve as a basic interpretation of the world. Similar to expert systems, product rule systems also make use of if-then statements. This means that product start only if and when a particular condition gets met. 

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What are the advantages of forward chaining?

The advantages of forward chaining are:

• Forward reasoning can be employed to draw multiple conclusions.
• Forward deduction provides a reasonable basis for reaching conclusions.
• There are no limits on the data derived from it. This makes it more flexible than backward chaining.

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What are the disadvantages of forward chaining?

The disadvantages of forward chaining are:

• Eliminate and synchronizing available data may consume a lot of time, which may stretch the process.
• The explanation of facts or observations in forward chaining may not be very clear.
• Forward chaining is only good to be used on problems that have a single starting point and multiple possible endpoints. It becomes rather inefficient when used on problems that have several starting points and just a single endpoint.

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What is the difference between forward chaining and backward chaining?

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Forward and backward chaining are the two most critical strategies in all of artificial intelligence. They lie in the Expert System Domain of AI. Inference Engines make use of the forward chaining and backward chaining strategies to make deductions.

In forward chaining, the inference engine applies inference rules on all the facts, conditions and derivations available in the knowledge base before it attempts to deduce the outcome. It  starts from an initial state and works to reach the goal (the final decision). You could say that forward chaining is the process or strategy that is used when decisions are taken on the basis of the data that is available.

Backward chaining refers to starting from the endpoint and moving towards the steps that led to the goal.The inference system knows the endpoint or the goal and works backwards to figure out which facts need to be asserted in order for the goal to be achieved. Backward chaining essentially works from the goals or the final decision and works to reach the initial state. Here, the endpoint is divided into sub-goals to prove the truth of facts. 

The differences between forward chaining and backward chaining in artificial intelligence are:

• While forward chaining is a data-driven approach, backward chaining is a goal-driven technique. It involves starting from the goal and reaching the initial state to derive the facts.
• Forward reasoning uses the Breadth-first strategy but backward chaining uses the Depth-first strategy for proof.
• Forward training is a more time-consuming process because it has to use all the rules. Backward chaining is a quicker process due to the fact that it only has to use a few rules.
• While forward reasoning is used in planning, monitoring, controlling, and interpreting applications, backward chaining is employed in automated inference engines, theorem proofs, proof assistants, as well as other artificial intelligence applications.  

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