Backward chaining

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A backward chaining (ger .: backward chaining ) referred to in the logic one inference - or inference strategy of the form: wenn Bedingung, dann Faktum

The opposite model is the forward chaining . These chains are important, for example, in the field of artificial intelligence for inference machines .

As with forward chaining, backward chaining is based on a transitive combination of rules . However, one starts with the target object and only checks the rules that have the target in the conclusion . If the value of an object is unknown in the premise of such a rule, an attempt is made to derive this from other rules. If this does not succeed, the value is finally requested from the user.

This process is also called goal-oriented inference . A related inference strategy is forward chaining .

Example of a backward-chaining rule interpreter

Working memory: X, Y, Z

Standard knowledge base:

1. X, Y -> S
2. S, Z -> T
3. S, Y -> A
4. Y, T -> B
5. X, T -> C

Goal: C is in memory

Conflict resolution:

1. Only use rules that write the symbol you are looking for into memory
   1. If these rules are not yet applicable, make them applicable (sub-goal)

Possible solution:

step	random access memory	Objective: C
1	X, Y, Z	Rule 5: X, T -> C
2	X, Y, Z	Sub-goal 1: X (in memory)
3	X, Y, Z	Sub-goal 2: T
4th	X, Y, Z	Rule 2: S, Z -> T
5	X, Y, Z	Sub-goal 3: S
6th	X, Y, Z	Rule 1: X, Y -> S
7th	X, Y, Z	Sub-goal 4: X (in memory)
8th	X, Y, Z, S	Sub-goal 5: Y (in memory)
9	X, Y, Z, S, T, C	Sub-goal 6: Z (in memory)

If you read the solution from "bottom to top", you will reach the goal: "C in memory".