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Training Multiple Agents to Work Together Using AI

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

Huy Tran , Neale Van Stralen , Seung Hyun Kim , Girish Chowdhary , University Of Illinois Urbana Champaign , Multiple Agents , Work Together Using , Illinois Urbana Champaign , Aerospace Engineer , Hyun Kim , Autonomous Agents , Multi Agent Systems ,

Promising AI traffic light system could mean the end of traffic jams

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

George Vogiatzis , Maria Chli , Aston University , Computer Science At Aston University , International Conference On Autonomous Agents , Vision Based Traffic Signal Control , International Conference , Autonomous Agents , Multi Agent Systems , Computer Science , Artificial Intelligence , Deep Reinforcement Learning , Deep Learning , Traffic Lights ,

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United States , Cumulocity Gm , Laura Wood , Koninklijke Philips , Facebook Inc , Ipsoft Inc , Oracle Corporation , Softbank Robotics Holding Corp , Microstrategy Incorporated , Qualcomm Incorporated , Devices Inc , Nokia Corporation , Ai Technology , It Corporation , Amazon Inc , Whirlpool Corporation , Digital Reasoning Systems Inc , Juniper Networks Inc , Xiaomi Technology Co , G Networks , Huawei Technologies Co , Atmel Corporation , Lg Electronics , Haier Group Corporation , Microsoft Corporation , Ai Market In Iot Networks ,

"BDI-Dojo: Developing robust BDI agents in evolving adversarial environ" by Simon Pulawski, Hoa Khanh Dam et al.

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 reinforcement learning against similarly learning-equipped adversarial agents. This adversarial training approach empowers BDI agents to become more resilient in uncertain, dynamic environments. ....

Adversarial Training , Multi Agent Systems , Reinforcement Learning ,

"Multi-Agent Learning Approaches in Open and Dynamic Environments" by Yuchen Wang

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. Specifically, this thesis tries to address three challenges, which are: 1. to accelerate multi-agent learning in open and dynamic environments. Current approaches for accelerating learning often require various agent abilities such as communication and wide ranges of observation. These abilities, however, may not always be satisfied in open and dynamic environments where agents have limited abilities in communication, observation, etc. To address the challenge, a multiagent learning approach based ....

Multi Agent Systems , Reinforcement Learning , Action Advice ,