In an expert system, a goal-oriented process of starting with a tentative conclusion and then looking for facts in the database that support that conclusion.
A technique of "reasoning" for an expert system in which the system tries to verify a hypothesis by verifying all statements that imply (or lead to) that hypothesis.
An inferencing technique related to data seeking, in which G2 seeks the value of a variable by invoking rules that can conclude the variable's value. Contrast with forward chaining.
The process of determining the value of a goal by looking for rules that can conclude the goal. Attributes in the premise of such rules may be made subgoals for further search if necessary. [| Tutorial
The process used to prove goals by comparing the goals to the facts or rules.
A problem-solving procedure that starts with a statement and a set of rules leading to the statement and then works backward, matching the rules with information from a database of facts until the statement can be either verified or proven wrong.
in Artificial Intelligence (AI) it is a form of reasoning that starts with the conclusion and works backward. The goal is broken into many sub goals or sub-sub goals which can be solved more easily. Is also known as the top-down approach and is one of the two main methods of reasoning when using inference rules. The other is forward chaining.
The process in rule-based systems that constructs a hypothesis, then tests it by working backward through the rules to see if the hypothesis is supported. An example in weather forecasting is to assume there will be afternoon thunderstorm activity, then determine whether the data support this assumption. See also goal-directed reasoning, forward chaining.