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FREE online courses on Expert Systems - Data Driven Reasoning

 

For many problems it is not possible to enumerate all of the possible answers before hand and have the system select the correct one. For example, configuration problems fall in this category. These systems might put components in a computer, design circuit boards, or lay out office space. Since the inputs vary and can be combined in an almost infinite number of ways, the goal driven approach will not work.

 

The data driven approach, or forward chaining, uses rules similar to those used for backward chaining, however, the inference process is different. The system keeps track of the current state of problem solution and looks for rules which will move that state closer to a final solution.

 

A system to layout living room furniture would begin with a problem state consisting of a number of unplaced pieces of furniture. Various rules would be responsible for placing the furniture in the room, thus changing the problem state. When all of the furniture was placed, the system would be finished, and the output would be the final state. Here is a rule from such a system which places the television opposite the couch.

 

IF

unplaced tv and

couch on wall(X) and

wall(Y) opposite wall(X)

THEN

place tv on wall(Y).

 

This rule would take a problem state with an unplaced television and transform it to a state that had the television placed on the opposite wall from the couch. Since the television is now placed, this rule will not fire again. Other rules for other furniture will fire until the furniture arrangement task is finished.

 

Note that for a data driven system, the system must be initially populated with data, in contrast to the goal driven system which gathers data as it needs it. Figure 1.2 illustrates the difference between forward and backward chaining systems for two simplified rules. The forward chaining system starts with the data of a=1 and b=2 and uses the rules to derive d=4. The backward chaining system starts with the goal of finding a value for d and uses the two rules to reduce that to the problem of finding values for a and b.


 

Figure 1.3: Four levels of data representation

 

 

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