Q4. Most useful production system databases will contain a...

Q4. Most useful production system databases will contain a large amount of informa- tion that is irrelevant to the given problem being worked on, and this can make their use rather inefficient. Three traditional solutions to this problem are: • Use backward chaining rather than forward chaining. • Restrict forward chaining to a selected sub-set of rules. • Rewrite the rule set using information from the goal, so that only relevant variable bindings (those belonging to a magic set) are considered during forward inference. Explain how each of these approaches work, and suggest circumstances under which each one would be preferable to the others.

Transcript

00:01 Three parts at part a we need to talk about the forward training for the given scenario the talk about what training it involved starting with known facts and repeatedly applying and repeatedly applying rules to infer new facts until no more rules fire until no more rules fire so it's step by step start with abc start with facts now apply rule 1 so it will be a d which result in adding d to the fact third apply rule 10 so here it is b and d to e which result at e adding to fact apply rule 6 here e to l which result adding l to the fact apply rule 7 i and l to z but we cannot add z because you don't have the i so here will not add z now apply rule 3 which is c to g which result adding g to the fact apply rule 5 g is greater than j so which result adding j to the fact apply rule 8 apply rule 8 where h and j to k but we cannot add k because you don't have h apply rule 4 so e and f and to h we cannot add h because we don't have f apply rule 11 g and m to n cannot add n because we don't have m apply rule 9 d to i we cannot add i because we don't have d now at last no more rule can be fired so here after forward changing the known facts are a b c d e g j and l so this is a forward chaining.

02:46 Now, let's talk about the b part of the question, which is backward chaining.

02:53 So to prove jet is true using backward training.

02:56 We start with a goal start with goal and try to find supporting facts and rules.

03:13 So here we want to prove that from rule 7 i and l to z we see that z can be true if z can true if i and l are true third step.

03:36 We can prove i by using rule 9, which is d to i which means we need to prove d for the force from rule 1, which is a to d we can prove d if we prove a why we can we can prove a because it is given as fact so by backward chaining we have proved z b to be true the rules to fire that are 7 which is i and l to z rule 9 d to i and rule 1, which is a to d.

04:21 So after backward chaining, we know that z is true now, let's move towards the c part of the question.

04:32 So here applications of forward and backward chaining.

04:37 So when we talk about the application of forward chaining the forward chaining is useful when you have set the initial facts when you have set of initial facts and want to infer more want to infer more fact based on the predefined rules...

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