Abstract
This paper describes a research work on Semantic Inference, which can be regarded as an extension of Grammar Inference. The main task of Grammar Inference is to induce a grammatical structure from a set of positive samples (programs), which can sometimes also be accompanied by a set of negative samples. Successfully applying Grammar Inference can result only in identifying the correct syntax of a language. With the Semantic Inference a further step is realised, namely, towards inducing language semantics. When syntax and semantics can be inferred, a complete compiler/interpreter can be generated solely from samples. In this work Evolutionary Computation was employed to explore and exploit the enormous search space that appears in Semantic Inference. For the purpose of this research work the tool LISA.SI has been developed on the top of the compiler/interpreter generator tool LISA. The first results are encouraging, since we were able to infer the semantics only from samples and their associated meanings for several simple languages, including the Robot language.
Highlights
Grammar Inference, called Grammar Induction or Grammatical Inference, is the process of learning grammar from examples, either positive and/or negative [1,2]
Evolutionary Algorithm enhanced with local search and a generalisation process, and used this to infer a wide range of Domain-Specific Language (DSL) grammars from programs in a variety of DSLs, including DSLs embedded in general purpose programming languages (GPLs) and extensions of GPLs
With the fundamental work on Semantic Inference further advances on Grammar Inference would be possible, which may have new applications in other areas of Computer Science (e.g., Spam Modelling, Bioinformatics, Speech Recognition, Protocol Security etc.), for easier development of DSLs for domain experts
Summary
Grammar Inference, called Grammar Induction or Grammatical Inference, is the process of learning grammar from examples, either positive (i.e., the grammar generates a string) and/or negative (i.e., the grammar does not generate a string) [1,2]. In our previous research we developed a memetic algorithm [7], called MAGIc (Memetic Algorithm, for Grammar Inference) [8,9,10], which is a population-based. With Semantic Inference we will be able to infer DSL formal specifications from given sample programs annotated with a meaning that can be provided even by domain-experts. With the fundamental work on Semantic Inference further advances on Grammar Inference would be possible, which may have new applications in other areas of Computer Science (e.g., Spam Modelling, Bioinformatics, Speech Recognition, Protocol Security etc.), for easier development of DSLs for domain experts.
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