This paper proposes a tentative and original survey of meeting points between Knowledge
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …

This paper is a plea for developing possibilistic learning methods that would be consistent
with if-then rule-based reasoning. The paper first recall the possibility theory-based handling …

Reinforcement Learning with Human Feedback (RLHF) is considered a standard approach
to fine-tuning Large Language Models (LLMs). However, such methods often face limitations …

In this paper, we present a machine learning based splitting heuristic for divide-and-conquer
parallel Boolean SAT solvers. Splitting heuristics, whether they are look-ahead or look-back …

Abstract Machine learning and logical reasoning have been the two foundational pillars of
Artificial Intelligence (AI) since its inception, and yet, until recently the interactions between …

The effectiveness of semantic technologies in ensuring interoperability is often hindered by
the preference for internally developed knowledge bases and the presence of diverse …

This paper is a plea for revisiting various existing approaches to the handling of data, for
classification purposes, based on a set-theoretic view, such as version space learning …

This special issue results from the work of a working group. This group was dedicated to an
inventory and a study of the meeting points between Knowledge Representation and …

Deep learning and symbolic AI (artificial intelligence) are at the stage of reconciliation to
promote development of AI's complex reasoning ability. As the widely used human IQ …