ABSTRACT: This paper introduces a methodology that enables the relational learning framework to incorporate quantitative data derived from experimental studies in microbial ecology. The focus of using ...

Inductive logic programming (ILP) and machine learning together represent a powerful synthesis of symbolic reasoning and statistical inference. ILP focuses on deriving interpretable logic rules from ...

Logic and probability provide two distinct frameworks for modeling how rational agents ought to draw inferences and learn from the available data in the face of uncertainty. The aim of this conference ...

Functional programming, as the name implies, is about functions. While functions are part of just about every programming paradigm, including JavaScript, a functional programmer has unique ...

Automation has become a crucial component in modern industries, streamlining processes and increasing efficiency. One of the fundamental programming methods for controlling automated systems is ladder ...

Abstract: Support logic programming and its practical implementation (Fril) integrates probabilistic and fuzzy uncertainty into logic programming using mass assignments. This paper presents a snapshot ...

The debate between programming languages revolves around the necessity of sticking to ladder logic for ease of troubleshooting versus adopting higher-level languages for enhanced functionality and ...

Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and ...