In the ever-changing world of decision-making, when game theory and reinforcement
learning (RL) come together, they create a fascinating combination that shows a new way to …

There is a growing interest in implementing artificial intelligence for operations research in
the industrial environment. While numerous classic operations research solvers ensure …

Reinforcement Learning (RL) provides a framework in which agents can be trained, via trial
and error, to solve complex decision-making problems. Learning with little supervision …

In this paper, we propose a novel approach of model-based learning on state-based
potential games (MB-SbPGs) that enables distributed self-optimization of manufacturing …

In this paper, we introduce novel gradient-based optimization methods for state-based
potential games (SbPGs) within self-learning distributed production systems. SbPGs are …

In recent years, many powerful software packages have been released on various aspects
of machine learning (ML). However, there is still a lack of holistic development environments …

One of the issues addressed by machine learning, with applications in various disciplines or
fields such as the health sector and the agricultural sector among others, involves data …

This article describes a novel game structure for autonomously optimizing decentralized
manufacturing systems with multi-objective optimization challenges, namely Distributed …

A critical review of data mining in education on the levels and aspects of education |
Emerald Insight Books and journals Case studies Expert Briefings Open Access Publish …

The integration of advanced robotic technologies with modern software platforms has
enabled significant advancements in industrial automation. This paper aims to introduce a …