Artificial Intelligence
An analysis of first-order logics of probability
Artificial Intelligence
Probabilistic reasoning in logic programming
Methodologies for intelligent systems, 5
Probabilistic logic programming
Information and Computation
Data mining in finance: advances in relational and hybrid methods
Data mining in finance: advances in relational and hybrid methods
Discovery of empirical theories based on the measurement theory
Minds and Machines - Machine learning as experimental philosophy of science
| Hi-index | 0.00 |
We consider predictionsprovided by Inductive-Statistical (I-S). inference. It was noted by Hempel that I-S inference is statistically ambiguous. To avoid this problem Hempel introduced the Requirement of Maximal Specificity (RMS). We define the formal notion of RMS in terms of probabilistic logic, and maximally specific rules (MS-rules), i. e. rules satisfying RMS. Then we prove that any set of MS-rules draws no contradictions in I-S inference, therefore predictions based on MS-rules avoid statistical ambiguity. I-S inference may be used for predictions in knowledge bases or expert systems. In the last we need to calculate the probabilistic estimations for predictions. Though one may use existing probabilistic logics or "quantitative deductions" to obtain these estimations, instead we define a semantic probabilistic inference and prove that it approximates logical inference in some sense. We also developed a program system 'Discovery' which realizes this inference and was successfully applied to the solution of many practical tasks.