The Ultimate Guide to Forward and Backward Chaining in AI

Forward and backward chaining are the two crucial strategies in the expert system domain of Artificial Intelligence. Inference engineers use them for the deduction of new information. While forward chaining is goal driven and begins with facts, backward chaining is data-driven, beginning with a goal. The process comprises more detailed aspects, further covered in the article.  

What is Forward Chaining?

Forward chaining, forward deduction or forward reasoning is a method involving inference or logical rules (facts) for data extraction. It is a bottom-up approach performed redundantly to reach the endpoint or goal (decision). It begins with evaluating existing information, followed by the manipulation based on the knowledge base. The existing information can be as facts, derivations, and conditions. 




Let us say we have the following:

Fact 1: A dog is up for adoption through person A. 

Fact 2: Person B is looking for a dog. 

Inference rule: If a dog is up for adoption and someone is looking to adopt it, that person is free to adopt it.

Here, the decision can be reached as person b can adopt the dog from person A. This is how forward chaining works to make a decision. 

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Advantages and Disadvantages of Forward Chaining

The strategies have its own set of advantages and disadvantages:


  • Suitable to draw multiple conclusions simultaneously 
  • Higher flexibility than backward chaining 
  • Reliable for conclusion 


  • Time-consuming due to data synchronization
  • The fact explanation is unclear 

What is Backward Chaining?

Backward chaining, backward deduction or backwards is also a reasoning method that acts in the reverse direction to forward chaining. The top-down approach involves using decisions or goals to reach the facts. The backward chaining is the backtracking process of finding usage in diagnostics, debugging and prescription. 




Let us take the same example.

Decision/Goal: Person B adopts a dog. 

Fact 1: A dog is up for adoption from Person A.

Fact 2: Person B is looking for a dog. 

Inference rule: If a person wants to adopt a dog, he can if there is any up for adoption. 

Here, the inference engine will begin with the goal and look if the conditions are met. If both conditions are met, the stated decision can be concluded. 

Advantages and Disadvantages of Backward Chaining

While backward chaining has its benefits, it also has certain drawbacks, both enlisted below:


  • Swifter than forward chaining 
  • Easier process 
  • Efficiently drives correct solutions 


  • Provides single answer 
  • Less flexibility 
  • Suitable only if the endpoint is known 
  • Difficult to execute 

Importance of Chaining in Artificial Intelligence

The key reasons dictating the importance of forward and backward chaining in artificial intelligence are: 

  • It provides a method to carry out automated reasoning that aids in deriving new information, making inferences and reaching conclusions. 
  • It helps in knowledge representation, organization and efficient processing.
  • Assists in inference and decision making
  • Serves as a fundamental for expert systems designed to emulate human expertise 
  • Helps in problem identification 

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Differences Between Forward and Backward Chaining

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


Forward Chaining 

Backward Chaining 

Begins from 



Ends at



Driven by






ASK questions 










To reach the goal

To get a higher quantity of facts


Planning, interpretation, control and monitoring 

Automated inference engines, proof assistants and theorem proofs

How to Implement Forward Chaining?

One can implement forward chaining through the stated step-wise procedure:

  • Step 1: Identify the facts and process the knowledge required for data structure
  • Step 2: Create initial working memory comprising all the required facts for the forward chaining process
  • Step 3: Start and iterate the loop till receiving new information or reaching the specific termination. Check the known facts for the goal. If matched, the process will terminate. Else, it will iterate over the rules in the knowledge base to find the conclusion. In case of a match between the current goal and the rule, recursively invoke the backward chaining process for conditions of that rule. Repeat steps 1, 2 and 3 til the goal is satisfied or a further conclusion is reached. 
  • Step 4: Determine the termination condition either as a number of iterations or by defining the goal. Output the final state of working memory to get the results or conclusions. 

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How to Implement Backward Chaining?

The stepwise procedure to implement backward chaining is:

  • Step 1: Define the knowledge base as per the requirements. 
  • Step 2: Set the goal or conclusion to begin the backward chaining process. 
  • Step 3: Start and iterate the functions. Check the facts to match the goal. Terminate the process if successful; else, iterate over the rules in the knowledge base. Search to match the conclusion with a current goal. Like forward chaining, recursively invoke the backward chaining process for the rule conditions if there is a match. Repeat the previous process until you reach the endpoint. 
  • Step 4: Determine the termination condition for the backward chaining process that can be any specific criteria. Output the final result after reaching the endpoint. 


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Frequently Asked Questions 

1. Can forward and backward chaining be combined? 

Yes, they can be combined. The approach is called bidirectional or hybrid chaining and is efficient and flexible in nature. 

2. What are some real-world applications of forward chaining? 

Fraud detection, intelligent recommendation and process monitoring and control are some of the applications of forward chaining. 

3. What are some real-world applications of backward chaining?

Medical diagnosis, legal reasoning and fault diagnosis are real-world applications of backward chaining. 

4. Are there any alternatives to forward and backward chaining?

Yes, the alternatives to chaining are machine learning and statistical methods, bayesian network, rule-based system, fuzzy logic and others. 

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