Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a
feedback loop with four core functionalities: monitoring, analyzing, planning, and execution …

Neuro-symbolic systems combine the abilities of neural perception and logical reasoning.
However, end-to-end learning of neuro-symbolic systems is still an unsolved challenge. This …

Bridging logical reasoning and deep learning is crucial for advanced AI systems. In this
work, we present a new framework that addresses this goal by generating interpretable and …

Many interpretable AI approaches have been proposed to provide plausible explanations for
a model's decision-making. However, configuring an explainable model that effectively …

Causal language modeling using the Transformer architecture has yielded remarkable
capabilities in Large Language Models (LLMs) over the last few years. However, the extent …

We introduce iterative reasoning through energy diffusion (IRED), a novel framework for
learning to reason for a variety of tasks by formulating reasoning and decision-making …

One of the challenges in Artificial Intelligence (AI) is to integrate fast, automatic, and intuitive
System-1 thinking with slow, deliberate, and logical System-2 thinking. While deep learning …

This paper presents a novel deep learning-based approach to solving 4x4 Sudoku puzzles,
by viewing Sudoku as a complex multi-level sequence completion problem. It introduces a …