FREE online courses on Expert Systems - Goal-Driven Reasoning

 

Goal-driven reasoning, or backward chaining, is an efficient way to solve problems that can be modelled as "structured selection" problems. That is, the aim of the system is to pick the best choice from many enumerated possibilities. For example, an identification problem falls in this category. Diagnostic systems also fit this model, since the aim of the system is to pick the correct diagnosis.

 

The knowledge is structured in rules which describe how each of the possibilities might be selected. The rule breaks the problem into sub-problems. For example, the following top level rules are in a system which identifies birds.

 

IF

family is albatross and

color is white

THEN

bird is laysan albatross.

IF

family is albatross and

color is dark

THEN

bird is black footed albatross.

 

The system would try all of the rules which gave information satisfying the goal of identifying the bird. Each would trigger sub-goals. In the case of these two rules, the sub-goals of determining the family and the color would be pursued. The following rule is one that satisfies the family sub-goal:

 

IF

order is tubenose and

size large and

wings long narrow

THEN

family is albatross.

 

The sub-goals of determining color, size, and wings would be satisfied by asking the user. By having the lowest level sub-goal satisfied or denied by the user, the system effectively carries on a dialog with the user. The user sees the system asking questions and responding to answers as it attempts to find the rule which correctly identifies the bird.

 

Figure 1.2: Difference between forward and backward chaining