Difference between forward chaining and backward chaining in Artificial Intelligence
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. Show
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. Source: eduCBA What are the properties of forward chaining?Here are the properties of forward chaining or forward reasoning:
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 systemsExpert 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 SystemsProduction 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. 3x your revenue with Chatbots and Live Chat Schedule a demo What are the advantages of forward chaining?The advantages of forward chaining are:
What are the disadvantages of forward chaining?The disadvantages of forward chaining are:
What is the difference between forward chaining and backward chaining?Forward chaining vs Backward Chaining 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:
What is the difference between forward chaining and backward chaining in AI?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.
What is the difference between forward and backward?The forward and backward reasoning are differentiated on the basis of their purpose and process, in which forward reasoning is directed by the initial data and intended to find the goal while the backward reasoning is governed by goal instead of the data and aims to discover the basic data and facts.
What is the use of forward chaining and backward chaining?As the name implies, forward chaining begins with known facts and moves forward by applying inference rules to extract more data, and it continues until it reaches the goal, whereas backward chaining begins with the goal and moves backward by applying inference rules to determine the facts that satisfy the goal.
What is backward chaining in AI?Backward chaining is the logical process of inferring unknown truths from known conclusions by moving backward from a solution to determine the initial conditions and rules. Backward chaining is often applied in artificial intelligence (AI) and may be used along with its counterpart, forward chaining.
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