Question

Rules-based approaches to AI are broadly considered to be a failure. Question 6 options:

True

False

Answer 1

Answer : True.

A rule-based system is like a human being born with fixed knowledge. The knowledge of that human being doesn’t change over time. This implies that, when this human being encounters a problem for which no rules have been designed, then this human gets stuck and so won’t be able to solve the problem. In a sense, the human being doesn’t even understand the problem.

That’s the dilemma of rule-based systems. Rule-based systems also cause other problems. For example, it’s tough (to nearly impossible) to add rules to an already large knowledge base without introducing contradicting rules. The maintenance of these systems then often becomes too time-consuming and expensive. As such, rule-based systems aren’t very useful for solving problems in complex domains or across multiple different but simple domains. Apart from that, in some situations (e.g., cancer detection in medical images), it’s not even possible to explicitly define rules in a programmatic or declarative way. That’s typically the showstopper for rule-based systems, and (usually) the point where learning systems get into the game.

So, the Rules-based approaches to AI are broadly considered to be a failure.