Difference between Backward and Forward Chaining.

Forward Chaining and Backward Chaining are the two most important strategies in the field of Artificial Intelligence and lie in the Expert System Domain of AI.
Forward and Backward chaining is the strategies used by the Inference Engine in making the deductions.

Inference Engine:
Inference Engine is a component of the expert system that applies logical rules to the knowledge base to deduce new information. It interprets and evaluates the facts in the knowledge base in order to provide an answer.
A knowledgebase is a structured collection of facts about the system’s domain.

Forward Chaining:
Forward Chaining the Inference Engine goes through all the facts, conditions and derivations before deducing the outcome i.e When based on available data a decision is taken then the process is called as Forwarding chaining, It works from an initial state and reaches to the goal(final decision).

Example:
A
A -> B
B
—————————–
He is running.
If he is running, he sweats.
He is sweating.

Backward Chaining:
In this, the inference system knows the final decision or goal, this system starts from the goal and works backwards to determine what facts must be asserted so that the goal can be achieved, i.e it works from goal(final decision) and reaches the initial state.

Example:
B
A -> B
A
—————————–
He is sweating.
If he is running, he sweats.
He is running.

Difference between Forwarding Chaining and Backward Chaining:

	Forward Chaining	Backward Chaining 1. When based on available data a decision is taken then the process is called as Forward chaining. Backward chaining starts from the goal and works backward to determine what facts must be asserted so that the goal can be achieved. 2. Forward chaining is known as data-driven technique because we reaches to the goal using the available data. Backward chaining is known as goal-driven technique because we start from the goal and reaches the initial state in order to extract the facts. 3. It is a bottom-up approach. It is a top-down approach. 4. It applies the Breadth-First Strategy. It applies the Depth-First Strategy. 5. Its goal is to get the conclusion. Its goal is to get the possible facts or the required data. 6. Slow as it has to use all the rules. Fast as it has to use only a few rules. 7. It operates in forward direction i.e it works from initial state to final decision. It operates in backward direction i.e it works from goal to reach initial state. 8. Forward chaining is used for the planning, monitoring, control, and interpretation application. It is used in automated inference engines, theorem proofs, proof assistants and other artificial intelligence applications. My Personal Notes arrow_drop_up Save Like Previous Difference between ANN, CNN and RNN Next Uni-variate Optimization vs Multivariate Optimization Recommended Articles Page : Difference between Machine learning and Artificial Intelligence 19, Feb 18 Difference between Supervised and Unsupervised Learning 19, Jun 18 Difference between Soft Computing and Hard Computing 14, Jun 19 Difference between Fuzzification and Defuzzification 28, Aug 19 Difference Between Architectural Style, Architectural Patterns and Design Patterns 15, Apr 20 Difference between IoT and Big Data 01, May 20 Difference Between HDFS and HBase 05, May 20 Difference between JDBC and Hibernate in Java 29, May 20 Difference between Batch Gradient Descent and Stochastic Gradient Descent 12, Jun 20 Difference Between Amazon EBS and Amazon EFS 04, Feb 21 Difference Between Crisp Set and Fuzzy Set 24, May 21 Differences between Cloud Servers and Dedicated Servers 16, May 20 Comparison Between Mamdani and Sugeno Fuzzy Inference System 21, May 20 Bridge the Gap Between Engineering and Your Dream Job - Complete Interview Preparation 01, May 21 How to Connect Between Devices Via Network Wire on Cisco Packet Tracer(Virtual Program)? 17, May 21 MongoDB AND operator ( $and ) 09, Apr 20 Command Injection Vulnerability and Mitigation 23, Apr 17 Basic SQL Injection and Mitigation with Example 28, Mar 17 Denial of Service and Prevention 22, Apr 17 Format String Vulnerability and Prevention with Example 23, Apr 17 Code Injection and Mitigation with Example 24, Apr 17 Path Traversal Attack and Prevention 25, Apr 17 Passwords and Cryptographic hash function 05, May 17 ML | Underfitting and Overfitting 23, Nov 17 Article Contributed By : VishwashVishwakarma @VishwashVishwakarma Vote for difficulty Current difficulty : Basic Easy Normal Medium Hard Expert Article Tags : Advanced Computer Subject Report Issue Writing code in comment? Please use ide.geeksforgeeks.org, generate link and share the link here. Load Comments Improve your Coding Skills with Practice Try It! We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy Got It ! Start Your Coding Journey Now! Login Register