Name
Abstract | ||
|---|---|---|
In the context of Solomonoff's Inductive Inference theory, Induction operator plays a key role in modeling and correctly predicting the behavior of a given phenomenon. Unfortunately, this operator is not algorithmically computable. The present paper deals with a Genetic Programming approach to Inductive Inference, with reference to Solomonoff's algorithmic probability theory, that consists in evolving a population of mathematical expressions looking for the ‘optimal' one that generates a collection of data and has a maximal a priori probability. Validation is performed on Coulomb's Law, on the Henon series and on the Arosa Ozone time series. The results show that the method is effective in obtaining the analytical expression of the first two problems, and in achieving a very good approximation and forecasting of the third. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11729976_3 | EuroGP |
Keywords | Field | DocType |
algorithmic probability theory,genetic programming approach,induction operator,inductive inference theory,algorithmically computable,inductive inference,henon series,analytical expression,arosa ozone time series,probabilistic induction,good approximation,ozone,time series,probability theory | Inductive logic programming,Algorithmic probability,A priori probability,Inductive reasoning,Kolmogorov complexity,Computer science,Theoretical computer science,Inductive probability,Solomonoff's theory of inductive inference,Artificial intelligence,Probabilistic logic | Conference |
Volume | ISSN | ISBN |
3905 | 0302-9743 | 3-540-33143-3 |
Citations | PageRank | References |
2 | 0.41 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ivanoe De Falco | 1 | 242 | 34.58 |
| Antonio Della Cioppa | 2 | 141 | 20.70 |
| D. Maisto | 3 | 146 | 11.20 |
| Ernesto Tarantino | 4 | 361 | 42.45 |