Name
Abstract | ||
|---|---|---|
The problem of practically feasible inductive inference of functions or other objects that can be described by means of an attribute grammar is studied in this paper. In our approach based on attribute grammars various kinds of knowledge about the object to be found can be encoded, ranging from usual input/output examples to assumptions about unknown object's syntactic structure to some dynamic object's properties. We present theoretical results as well as describe the architecture of a practical inductive synthesis system based on theoretical findings. |
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/3-540-49730-7_28 | ALT |
Keywords | Field | DocType |
unknown object,inductive inference search space,attribute grammars,theoretical finding,theoretical result,practical inductive synthesis system,syntactic structure,dynamic object,output example,feasible inductive inference,usual input,attribute grammar,input output,inductive inference,search space | Rule-based machine translation,Inductive reasoning,Attribute grammar,Terminal and nonterminal symbols,L-attributed grammar,Context-free grammar,Formal language,Computer science,Inference,Artificial intelligence,Natural language processing,Machine learning | Conference |
Volume | ISSN | ISBN |
1501 | 0302-9743 | 3-540-65013-X |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ugis Sarkans | 1 | 948 | 122.35 |
| Janis Barzdins | 2 | 199 | 35.69 |