Multi Agent Systems News Today

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Agent-based scheduling refers to applying Multi-Agent System (MAS) to model the scheduling environment, where intelligent agents cooperate to determine the task scheduling, so as to achieve scheduling objectives autonomously. To date, agent-based scheduling approaches have been developed for multiple domains, such as cloud computing, manufacturing systems, and smart grids. However, with the applications of the Internet of Things (IoT) and advanced communication technologies, real-world schedulin...

This article discusses the two most promising AI cryptos that are seeing increased interest from investors: Fetch.ai (FET) and SingularityNET (AGIX). Meanwhile, TMS Network (TMSN) continues to gain attraction among investors.

This algorithm is applicable to surveillance, warehouse robots, traffic control, and power grid control

When communication lines tend to be open, separate agents like drones or robots could work jointly to collaborate and finish a task.

Aston University researchers have developed an artificial intelligence system to manage traffic more efficiently at intersections.

The Belief-Desire-Intention (BDI) architecture is a widely-used model for developing multi-agent systems. BDI agents pursue their goals over time using a collection of plan recipes that are programmed by the developers. Thus, traditional BDI agents are limited in dealing with dynamic environments where uncertainties are not known beforehand, such as those introduced by adversarial forces. In this paper, we present the BDI-Dojo framework for developing robust BDI agents by training them using rei...

Multi-agent learning has been widely used to enable multiple agents to autonomously find solutions for complex tasks such as robotic swarm control, social order maintenance, and transportation management. To date, various multi-agent learning approaches have been developed with various capabilities such as having teaching skills and utilising collective intelligence. Despite the progress, there are still various research challenges to be addressed to advance the usage of multi-agent learning. Sp...