

![0_image_0.png](0_image_0.png)

head(X,Y)
nonvar(X), male(X), p1(X), p3(X,Y), p2(Y).

head(X,Y)
nnvar(X), not(male(X)), p1(X), p2(Y), p3(X,Y).

head(X,Y)
var(X), nonvar(Y), p2(Y), p3(X,Y), p1(X).

head(X,Y)
var(X), var(Y), p3(X,Y), p1(X), p2(Y).

we can see that while the DAC algorithm helped to reduce the inference rom Table time b a factor of , the total time was reduced onl  b  a factor of 1 .  This di erence is caused b the additional computation of the ordering procedure. There is a danger that the benefit obtained b  ordering will be outweighed b  the cost of the ordering process.

This is a manifestation of the so called utility problem (  inton, 19 arkovitch cott, 199). In s  stems that are strongl moded (such as   ercur     omog iet al., 199 b) we can emplo the DAC algorithm staticall at compilation time for each one of the available modes, thus reducing the run time ordering time to  ero. The mode based approach performs onl s ntactic tests of the subgoal arguments. The classification tree method, described above, is a generali ation of the mode based approach, allowing semantic tests as well.

ue to insu cient learning e perience or lack of meaningful semantic tests, it is   uite possible that the classification trees contain leaves with large degrees of error. In such cases we still need to perform the ordering d  namicall .  To reduce the harmfulness of the utilit problem in the case of d  namic ordering, we can use a cost sensitive variation of the DAC
arkovitch, 199 a, 199 b). This modified algorithm deals with the algorithm ( edeniov problem b  e  plicit  reasoning  about the econom  of the control process. The algorithm is anytime, that is, it can be stopped at an moment and return its currentl best ordering
( odd ean, 199).    e learn a resource-investment function to compute the e pected return in speedup time for additional control time. This function is used to determine a stopping condition for the an time procedure.    e have implemented this framework and found that indeed we have succeeded in reducing ordering time, without significant increase of inference time.