Difference Between Forward and Backward Reasoning in AI

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In this post, we will understand the difference between forward reasoning and backward reasoning in AI −

Forward Reasoning

  • It is a data-driven task.

  • It begins with new data.

  • The object is to find a conclusion that would follow.

  • It uses an opportunistic type of approach.

  • It 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.

  • The first step is that the system is given one or more constraints.

  • The rules are searched for in the knowledge base for every constraint.

  • The rule that fulfils the condition is selected.

  • Every rule can produce new condition from the conclusion which is obtained from the invoked one.

  • New conditions can be added, and are processed again.

  • The step ends if no new conditions exist.

  • It may be slow,

  • It follows top-down reasoning.

Backward Reasoning

  • It is a goal driven task.

  • It begins with conclusions that are uncertain.

  • The objective is to find the facts that support the conclusions.

  • It uses a conservative type of approach.

  • It flows from consequence to the incipient.

  • The system helps choose a goal state, and reasons in a backward direction.

  • First step is that the goal state and rules are selected.

  • 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.

  • 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.

  • It tests less number of rules.

  • It provides small amount of data.

  • It follows bottom-up reasoning technique.

  • It contains less number of initial goals and has large number of rules.

  • It is based on the decision fetched by the initial state.

  • It is also known as a decision-driven or goal-driven inference technique.

  • The system selects a goal state and reasons in the backward direction.

Updated on 29-Apr-2021 06:13:56