Strong monotonic and set-driven inductive inference

A Hybrid Approach to Inference in Probabilistic Non-Monotonic Logic Programming

We present a probabilistic inductive logic programming framework which integrates non-monotonic reasoning, probabilistic inference and parameter learning. In contrast to traditional approaches to probabilistic Answer Set Programming (ASP), our framework imposes only comparatively little restrictions on probabilistic logic programs in particular, it allows for ASP as well as FOL syntax, and for ...

PrASP Report

This technical report describes the usage, syntax, semantics and core algorithms of the probabilistic inductive logic programming framework PrASP. PrASP is a research software which integrates non-monotonic reasoning based on Answer Set Programming (ASP), probabilistic inference and parameter learning. In contrast to traditional approaches to Probabilistic (Inductive) Logic Programming, our fra...

Nusselt Number Estimation along a Wavy Wall in an Inclined Lid-driven Cavity using Adaptive Neuro-Fuzzy Inference System (ANFIS)

In this study, an adaptive neuro-fuzzy inference system (ANFIS) was developed to determine the Nusselt number (Nu) along a wavy wall in a lid-driven cavity under mixed convection regime. Firstly, the main data set of input/output vectors for training, checking and testing of the ANFIS was prepared based on the numerical results of the lattice Boltzmann method (LBM). Then, the ANFIS was develope...

Inductive Inference and Nonmonotonic Reasoning

On the Power of Monotonic Language Learning

In the present paper strong{monotonic, monotonic and weak{monotonic reasoning is studied in the context of algorithmic language learning theory from positive as well as from positive and negative data. Strong{monotonicity describes the requirement to only produce better and better generalizations when more and more data are fed to the inference device. Monotonic learning re ects the eventual in...