Common Proceed Modes of Inference Engine and Solving with Forward Chaining and Backward Chaining
What are the common proceed modes of Inference engine?
How do forward chaining and backward chaining help solve for the given knowledge base and goal?
Answer:
Forward chaining and backward chaining are common proceed modes of inference engines. Forward chaining starts with the given facts and applies rules to derive new conclusions, while backward chaining starts with the goal and works backward to find the facts that support the goal. In the given knowledge base, using forward chaining, we can conclude that John is colored brown based on his behavior. Using backward chaining, we can conclude that John is colored yellow based on his behavior.
Explanation:
Common Proceed Modes of Inference Engine and Solving with Forward Chaining and Backward Chaining:
Inference engines are components of artificial intelligence systems that reason and make logical deductions based on given knowledge. They are used to draw conclusions or make predictions based on a set of rules or facts. There are two common proceed modes of inference engines: forward chaining and backward chaining.Forward Chaining:
Forward chaining starts with the given facts and applies rules to derive new conclusions. It is a data-driven approach where the inference engine starts with the available data and works towards the goal. In this mode, the engine matches the conditions of the rules with the available data and applies the rules to generate new facts. This process continues until the goal is reached or no more rules can be applied.
Backward Chaining:
Backward chaining, on the other hand, starts with the goal and works backward to find the facts that support the goal. It is a goal-driven approach where the inference engine starts with the goal and tries to find the rules and facts that lead to the goal. It recursively applies rules and checks if the conditions of the rules are satisfied. If not, it continues to backtrack until it finds the necessary facts.
Solving the Given Knowledge Base and Goal:
In the given knowledge base, we have rules that define the characteristics of different animals based on their behaviors and colors. The goal is to determine the color of John based on his behavior. We can use both forward chaining and backward chaining to solve this problem.
By utilizing both modes of inference engines, we can successfully determine the color of John as per the given knowledge base and goal.