It uses backtracking to find the most optimal way for conflict resolution or to reach the goal state, wherein the search starts from the conclusion and goes back to understand the conditions from which it resulted. What is Backward Chaining in Expert System?Д«ackward Chaining or backward reasoning is the inference engine reasoning technique, where chaining starts from a hypothetical goal. Now that we understand what Forward Chaining is, let us move on to discussing Backward Chaining. Might give different conclusions, which may result in the high cost of the chaining process.The hypothesis performed by the forward chaining is at times not clear.It requires excessive user inputs, much of which remains irrelevant to the final conclusion.It generates new information without understanding what is important and relevant to the problem solution.Since forward chaining is among the first few AI reasoning techniques developed to help AI machines perform reasoning, there are certain drawbacks associated with it, like: Here, the reception of new data triggers new inferences, which makes the engine more suitable for dynamic situations.It helps find possible conclusions supported by the given facts.Runs great when a problem naturally begins by collecting data and searching for information that can be collected.It provides excessive data from the available few initial data or facts.Best choice for expert systems that need interruption, control, monitoring, and planning.It is useful when there are a few or initial states.Benefits of Forward Chaining:ДЇrom assessing the known facts to obtaining logical conclusions, there are numerous benefits offered by the forward chaining reasoning technique, a few of which are mentioned below: This process is most evidently used when Forward Chaining is implemented in rule-based systems. This process is continued until no further matches are found.Once the new cycle is initiated, rules that were previously fired are ignored.The system evaluates the rules again to identify new matches.The inference engine scans the rules in a predefined sequence looking for the one that matches contents in the working memory.This information is then sealed in the working memory.The user provides the system with information about the problem.The forward-chaining algorithm, which is also known as the forward chaining inference process, uses the following step by step process to complete a task and reach the logical or accurate conclusion: Uses prolog language, the logic programming language, to deal with inference rules.It is a data-driven approach that uses available data to reach the goal state.Used to make conclusions based on known facts or data, moving from the initial state to the goal state.This a bottom-up approach that moves from the bottom to the top.It is also known as the Forward Deduction.Other important properties that define forward chaining are:

Backwards chaining