Semantic and Syntactic Attribute Types in AQ Learning

Abstract

AQ learning strives to perform natural induction that aims at deriving general descriptions from specific data and formulating them in human-oriented forms. Such descriptions are in the forms closely corresponding to simple natural language statements, or are transformed to such statements in order to make computer generated knowledge easy to interpret and understand. An important feature of natural induction is that it employs a wide range of types of attributes to guide the process of generalization. Attribute types constitute problem domain knowledge, and are provided by the user, or are inferred by the learning program from the data. This paper makes a distinction between semantic and syntactic attribute types in AQ learning, explains their relationships and provides their classifications. Semantic types depend solely on the structure of attribute domains and help to create plausible generalizations, while syntactic types depend also on physical properties of attribute domains, and are used to efficiently implement semantic types.