Explain the different inference mechanisms used in expert systems and their application in rule-based reasoning.

In expert systems, inference mechanisms are the methods by which conclusions are drawn from the available knowledge to provide solutions or make decisions. These mechanisms play a vital role in the reasoning process of expert systems and are particularly important in rule-based reasoning. Here, we will discuss different inference mechanisms used in expert systems and their application in rule-based reasoning. 1. Forward Chaining: * Forward chaining, also known as data-driven reasoning, starts with the available facts and uses rules to derive new conclusions or facts. * It involves matching the conditions of rules with the known facts, firing applicable rules, and adding the consequent actions or conclusions to the knowledge base. * In rule-based reasoning, forward chaining is typically used when the system has a large number of facts and wants to derive new knowledge from them. * It is particularly useful when the system needs to analyze real-time data or respond to changing conditions. 2. Backward Chaining: * Backward chaining, also known as goal-driven reasoning, starts with a specific goal or conclusion and works backward to determine the facts or rules that support it. * It involves matching the desired goal with the consequent actions or conclusions of th....