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Backward chaining's goal oriented behavior is efficient because it
avoids requests for input that won't contribute to determining the
value of the consultation's goal. As a result, it provides the foundation for
most rule-based expert systems. Backward chaining systems are described as
hypothesis driven because they operate by selecting successive
rules that can determine the value of a goal or subgoal: this value
becomes the hypothesis to be proven or disproven. |
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In some interview scenarios it is natural to collect data in advance,
perhaps using a paper questionnaire. In other cases input to an expert
system is collected automatically, perhaps using sensors on a machine. For
these two situations the forward chaining approach makes sense.
Forward chaining systems are described as data driven because they
deduce everthing they can from a set of data rather than working backward
from an hypothesis. |
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To provide the most flexibility, many expert system shells support
both forward and backward chaining even in the same interview. For
example, some initial data might be requested and forward chained before
the backward chaining operation of the inference engine is started.
The inference engine's control capabilities enable this flexibility. |
Expert systems are often able to deal with attributes that are
assigned values with some degree of uncertainty. We'll turn now to a
discussion of where this processing is needed and how it is accomplished. |