Difference between Forward and Backward Reasoning in AI

Differences Artificial Intelligence Machine Learning

The objective of search in Artificial Intelligence (AI) is to find the path to solve different problems. The search in AI can be executed in two ways namely, Forward Reasoning and Backward Reasoning. The most basic difference between the two is that forward reasoning starts with the new data to find conclusions, whereas backward reasoning starts with a conclusion to determining the initial data.

Read this article to learn more about Forward Reasoning and Backward Reasoning and how they are different from each other.

What is Forward Reasoning?

Forward reasoning is a process in artificial intelligence that finds all the possible solutions of a problem based on the initial data and facts. Thus, the forward reasoning is a data-driven task as it begins with new data. The main objective of the forward reasoning in AI is to find a conclusion that would follow. It uses an opportunistic type of approach.

Forward reasoning flows from incipient to the consequence. The inference engine searches the knowledge base with the given information depending on the constraints. The precedence of these constraints have to match the current state.

In forward reasoning, the first step is that the system is given one or more constraints. The rules are then searched for in the knowledge base for every constraint. The rule that fulfils the condition is selected. Also, every rule can generate a new condition from the conclusion which is obtained from the invoked one. This new conditions can be added and are processed again.

The step ends if no new conditions exist. Hence, we can conclude that forward reasoning follows the top-down approach.

What is Backward Reasoning?

Backward reasoning is the reverse process of the forward reasoning in which a goal or hypothesis is selected and it is analyzed to find the initial data, facts, and rules. Therefore, the backward reasoning is a goal driven task as it begins with conclusions or goals that are uncertain. The main objective of the backward reasoning is to find the facts that support the conclusions.

Backward reasoning uses a conservative type of approach and flows from consequence to the incipient. The system helps to choose a goal state and reasons in a backward direction. The first step in the backward reasoning is that the goal state and rules are selected. Then, sub-goals are made from the selected rule, which need to be satisfied for the goal state to be true.

The initial conditions are set such that they satisfy all the sub-goals. Also, the established states are matched to the initial state provided. If the condition is fulfilled, the goal is the solution, otherwise the goal is rejected. Therefore, backward reasoning follows bottom-up technique.

Backward reasoning is also known as a decision-driven or goal-driven inference technique because the system selects a goal state and reasons in the backward direction.

Difference between Forward and Backward Reasoning in AI

The following are the important differences between Forward and Backward Reasoning in AI −

S.No.	Forward Reasoning	Backward Reasoning
1.	It is a data-driven task.	It is a goal driven task.
2.	It begins with new data.	It begins with conclusions that are uncertain.
3.	The objective is to find a conclusion that would follow.	The objective is to find the facts that support the conclusions.
4.	It uses an opportunistic type of approach.	It uses a conservative type of approach.
5.	It flows from incipient to the consequence.	It flows from consequence to the incipient.
6.	Forward reasoning begins with the initial facts.	Backward reasoning begins with some goal (hypothesis).
7.	Forward reasoning tests all the rules.	Backward reasons tests some rules.
8.	Forward reasoning is a bottom-up approach.	Backward reasoning is a top-down approach.
9.	Forward reasoning can produce an infinite number of conclusion.	Backward reasoning produces a finite number of conclusions.
10.	In the forward reasoning, all the data is available.	In the backward reasoning, the data is acquired on demand.
11.	Forward reasoning has a small number of initial states but a large number of conclusions.	Backward reasoning has a smaller number of goals and a larger number of rules.
12.	In forward reasoning, the goal formation is difficult.	In backward reasoning, it is easy to form a goal.
13.	Forward reasoning works in forward direction to find all the possible conclusions from facts.	Backward reasoning work in backward direction to find the facts that justify the goal.
14.	Forward reason is suitable to answer the problems such as planning, control, monitoring, etc.	Backward reasoning is suitable for diagnosis like problems.

Conclusion

The most significant difference between the two approaches is that forward reasoning is a data-driven task, while backward reasoning is a goal-driven task.