Titlebook: Agents and Artificial Intelligence; 13th International C Ana Paula Rocha,Luc Steels,Jaap van den Herik Conference proceedings 2022 Springer

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Negotiation Considering Privacy Loss on Asymmetric Multi-objective Decentralized Constraint Optimizach areas including automated negotiating agents and Distributed Constraint Optimization Problems (DCOPs). Although one of recent approaches for privacy preservation is applying secure computation to negotiation process, publishing some information during the negotiation is necessary in the cases whe

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Assured Deep Multi-Agent Reinforcement Learning for?Safe Robotic Systemsmany scenarios. However, this is not the case in safety-critical scenarios, where the reinforcement learning process, which uses stochastic mechanisms, could lead to highly unsafe outcomes. We proposed a novel, safe multi-agent reinforcement learning approach named Assured Multi-Agent Reinforcement

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Informer: An Efficient Transformer Architecture Using Convolutional Layersd of Natural Language Processing (.). However, despite the advantages of this architecture, it has some negative factors, such as the high number of parameters it uses. That is why the use of this type of architecture can become expensive for research teams with limited resources. New variants have