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 ...

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 ...

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 ...

Mechanistically interpretable neurosymbolic AI (Nature Comput Sci 2024): losslessly compressing NNs to computer code and discovering new algorithms which generalize out-of-distribution and outperform ...

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 ...