Authors:
João Fabrício Filho
1
;
Luis Gustavo Araujo Rodriguez
2
and
Anderson Faustino da Silva
2
Affiliations:
1
Universidade Tecnológica Federal do Paraná and Universidade Estadual de Maringá, Brazil
;
2
Universidade Estadual de Maringá, Brazil
Keyword(s):
Knowledge Representation, Program Representation, Reasoning System, Compiler, Code Generation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Case-Based Reasoning
;
Enterprise Information Systems
;
Pattern Recognition
;
Problem Solving
;
Strategic Decision Support Systems
;
Symbolic Systems
;
Theory and Methods
Abstract:
Knowledge representation attempts to organize the knowledge of a context in order for automated systems
to utilize it to solve complex problems. Among several difficult problems, one worth mentioning is called
code-generation, which is undecidable due to its complexity. A technique to mitigate this problem is to
represent the knowledge and use an automatic reasoning system to infer an acceptable solution. This article
evaluates knowledge representations for program characterization for the context of code-generation systems.
The experimental results prove that program Numerical Features as knowledge representation can achieve 85% near to the best possible results. Furthermore, such results demonstrate that an automatic code-generating system, which uses this knowledge representation is capable to obtain performance better than others code-generating
systems.