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Inference engine
The inference engine is the brain of the expert system.
Inference commonly proceeds by:
- forward chaining
- backward chaining
Forward chaining or data-driven inference works from an initial state, and by looking at the conditions of the rules, calculate conclusions, possibly updating the knowledge base or working memory.
This continues until no more rules can be applied or some cycle limit is met, e.g.
Disadvantages of forward chaining:
- Many rules may be applicable. The whole process is not directed towards a goal.
Backward chaining or goal-driven inference works towards a final state, and by looking at the working memory to see if the goal already there. If not look at the conclusions of rules that will establish goal, and set up subgoals for achieving conditions of the rules.
This continues until some rule can be applied, apply to achieve goal state.
Advantage of backward chaining:
- Search is directed
Disadvantages of backward chaining:
- Goal has to be known
Source of information: Introduction to AI — Week 2