Forward Chaining

What is forward chaining or forward reasoning?

Forward chaining is a form of reasoning while using an inference engine. It is also called forward deduction or forward reasoning.

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

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.

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.

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.


What is the difference between forward chaining and backward chaining?

Backward chaining refers to starting from the endpoint and moving towards the steps that led to the goal. Here, the endpoint is divided into sub-goals to prove the truth of facts.

The differences between forward chaining and backward chaining 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 Bread-first strategy but backward chaining uses the Depth-first strategy.
  • Forward training is more time-consuming because it has to use all the rules. Backward chaining is quicker because 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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Forward Chaining

October 14, 2020

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What is forward chaining or forward reasoning?

Forward chaining is a form of reasoning while using an inference engine. It is also called forward deduction or forward reasoning.

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

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.

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.

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.


What is the difference between forward chaining and backward chaining?

Backward chaining refers to starting from the endpoint and moving towards the steps that led to the goal. Here, the endpoint is divided into sub-goals to prove the truth of facts.

The differences between forward chaining and backward chaining 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 Bread-first strategy but backward chaining uses the Depth-first strategy.
  • Forward training is more time-consuming because it has to use all the rules. Backward chaining is quicker because 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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