Knowledge base tree structure

FIGURE 1. ERICE Knowledge Base tree structure. 

The last step in the preceding argument, the use of our knowledge about flowshop scheduling, turns what had been a mainly syntactic criterion over the tree structure of the example, into a criterion based on state variables of (jc, y). The state variable values, the completion times of the various flowshop machines, are accessible before the subtrees beneath jc and y have been generated. Indeed, they determine the relationships between the respective elements of the subtrees (jcm, yu). If we can formalize the process of showing that the pair (jc, y) identified with our syntactic criterion, satisfies the eonditions for equivalence or dominance, wc will in the process have generated a new equivalence rule. 

Many of the facts in the system are shared by several subproblems, and subproblems must be. developed by starting at the top of the hierarchy and working down. Other than these stipulations, they are independent problems. Each branch of the tree can be used independently, and need not be complete to be useful in the formulation study. The expert system s competence on each subproblem can be judged independently. In many cases different experts are used to develop the knowledge bases for different subproblems. Figure 1 shows the structure of the problem, tracing one branch from each level. 

Finally, the structure of the hierarchy itself is at odds with schema theory. The schema is a network of related knowledge, not a branching tree with distinct levels. A great deal more connectivity exists in the schema than in the learning hierarchy, and instruction based on schemas focuses directly on these connections. 

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