Diabeto led to a significant improvement of dietetic, knowledge in group A (P less than 0.0005) and also to improved dietary habits; decreased caloric intake in patients initially overeating (P less than 0.05), increase of dietary carbohydrate from 39.7 +/- 0.7 to 42.9 +/- 0.9% in patients with an initial intake less than 45% carbohydrate, and decrease of fat intake from 41.9 +/- 0.9 to 37.4 +/- 1.1% in patients with an initial intake of greater than 35% fat (P less than 0.0005). In the second study, in addition to similar improvements to those observed in the first study, HbA1 decreased from 11.0 +/- 0.4 to 9.9 +/- 0.4% (P less than 0.005) and fructosamine from 5.00 +/- 0.17 to 4.57 +/- 0.17% (P less than 0.001). Diabeto appears to be an effective therapeutic tool in the control of metabolic diseases.

Pattern‐directed inference systems (P.D.I.S.) are among the most largely used tools in A.I. to‐day in order to represent and exploit knowledge. Generally, P.D.I.S.'s use production rules triggered by matching between rule patterns and elements of the data base. However, the lack of flexibility in the matching remains a drawback in this kind of system. In the framework of the communication in natural language with robots, approximate descriptions of real world situations and approximately specified rules are needed; furthermore, similarity in the matching process does not always need to be perfect. Thus, the pervading fuzziness of natural language can be taken into account. The following levels, belonging to the real interval [0,1], are evaluated: The possibility of similarity between referents designated in the data and in the pattern respectively; the necessity that a referent designated in the data is similar to a referent designated in the pattern. Designations are fuzzy when the pattern or the data are fuzzy, which is usual with words of a natural language.

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