Introduction
In the field of Artificial Intelligence (AI), especially in rule-based expert systems, inference engines use two major strategies to derive conclusions: Forward Chaining and Backward Chaining. Both methods are applied to derive facts from rules but in opposite directions of reasoning.
Forward Chaining
Forward chaining is a data-driven inference technique. It starts with the known facts and applies inference rules to extract more data (conclusions) until the goal is reached.
Working:
- Starts with initial facts.
- Applies rules to infer new facts.
- Continues until the desired goal is achieved or no more rules can be applied.
Example:
Rules:
- R1: If it rains, then the ground gets wet.
- R2: If the ground is wet, then traffic will be slow.
Facts:
- It is raining.
Inference (Forward Chaining):
- Apply R1: Ground gets wet.
- Apply R2: Traffic will be slow.
Conclusion: Traffic will be slow.
Backward Chaining
Backward chaining is a goal-driven inference method. It begins with the goal (what you want to prove) and works backward by identifying rules that could lead to the goal and checking whether the premises of those rules are true.
Working:
- Start from the goal (hypothesis).
- Find rules that lead to the goal.
- Check if the conditions of those rules can be satisfied using existing facts.
Example:
Goal: Is the traffic slow?
Rules:
- R1: If the ground is wet, then traffic will be slow.
- R2: If it rains, then the ground gets wet.
Check:
- To prove traffic is slow → need to prove ground is wet (R1)
- To prove ground is wet → need to prove it rained (R2)
- If “It rained” is a known fact, goal is proven.
Conclusion: Backward chaining validates the goal by tracing back the rules and checking known facts.
Comparison Table
| Feature | Forward Chaining | Backward Chaining |
|---|---|---|
| Direction | Data-driven | Goal-driven |
| Start Point | Known facts | Target goal |
| Usage | Suitable for discovering all facts | Suitable for checking specific goals |
| Efficiency | May be slower for large datasets | More efficient for specific conclusions |
Conclusion
Forward and Backward chaining are essential reasoning methods in AI. Forward chaining is useful when all consequences need to be derived from known data, while backward chaining is efficient when checking if a specific conclusion can be drawn. Both are fundamental to rule-based systems and AI inference engines.