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