In this paper we are proposing an approach for coordinating a team of homogeneous agents based on a flexible common Team Strategy as well as on the concepts of Situation Based Strategic Positioning and Dynamic Positioning and Role Exchange. We also introduce an Agent Architecture including a specific high-level decision module capable of implementing this strategy. Our proposal is based on the formalization of what is a team strategy for competing with an opponent team having opposite goals. A team strategy is composed of a set of agent types and a set of tactics, which are also composed of several formations. Formations are used for different situations and assign each agent a default spatial positioning and an agent type (defining its behaviour at several levels). Agent’s reactivity is also introduced for appropriate response to the dynamics of the current situation. However, in our approach this is done in a way that preserves team coherence instead of permitting uncoordinated agent behaviour. We have applied, with success, this coordination approach to the RoboSoccer simulated domain. The FC Portugal team, developed using this approach won the RoboCup2000 (simulation league) European and World championships scoring a total of 180 goals and conceding none.

Laser-based additive manufacturing (LAM) offers the ability to produce near-net-shape parts with unparalleled energy efficiency and flexibility in both geometry and material selection. Despite their advantages, these processes are inherently complex and are characterized by multiphysics interactions, multiscale phenomena, and highly dynamic behaviors, making their modeling and optimization particularly challenging. Artificial intelligence (AI) has emerged as a promising tool for enhancing the monitoring and control of additive manufacturing. This paper presents a systematic review of AI applications for real-time control of laser-based manufacturing processes, analyzing 11 relevant articles sourced from the Scopus and IEEE Xplore databases. The ultimate aim of this work is to contribute to the advancement of autonomous manufacturing systems - capable of self-monitoring and self-correction to ensure optimal part quality, enhanced efficiency, and reduced human intervention. By defining a groundwork for future developments, this review not only highlights current advancements but also paves the way for continued innovation and progress to develop AI-based controllers in manufacturing.

Intelligent wheelchairs (IWs) can become an important solution to the challenge of assisting individuals who have disabilities and are thus unable to perform their daily activities using classic powered wheelchairs. This article describes the concept and design of IntellWheels, a modular platform to facilitate the development of IWs through a multiagent system paradigm. In fact, modularity is achieved not only in the software perspective, but also through a generic hardware framework that was designed to fit, in a straightforward manner, almost any commercial powered wheelchair. Experimental results demonstrate the successful integration of all modules in the platform, providing safe motion to the IW. Furthermore, the results achieved with a prototype running in autonomous mode in simulated and mixed-reality environments also demonstrate the potential of our approach. Although some future research is still necessary to fully accomplish our objectives, preliminary tests have shown that IntellWheels will effectively reduce users' limitations, offering them a much more independent life.