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Titlebook: Agents and Artificial Intelligence; 12th International C Ana Paula Rocha,Luc Steels,Jaap van den Herik Conference proceedings 2021 Springer

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41#
發(fā)表于 2025-3-28 15:06:12 | 只看該作者
Logos, Pathos, and Entertainment the hierarchy of semantic level from the data, neural networks often outperform other techniques in complex issues. However, to perform well, the models need a vast amount of data, which is not always available. To overcome that problem, we propose an approach of injecting knowledge into the neural
42#
發(fā)表于 2025-3-28 18:57:25 | 只看該作者
43#
發(fā)表于 2025-3-28 23:23:06 | 只看該作者
44#
發(fā)表于 2025-3-29 04:28:07 | 只看該作者
45#
發(fā)表于 2025-3-29 11:06:46 | 只看該作者
https://doi.org/10.1007/978-3-030-77431-8 the show according to the participant feedback. Such systems usually need computer vision algorithms whose performance are severely affected by constraints such as illumination and cameras position. In this paper we present a fully automatic audience engagement system, optimized for live music even
46#
發(fā)表于 2025-3-29 12:57:02 | 只看該作者
47#
發(fā)表于 2025-3-29 17:04:41 | 只看該作者
Intelligent Local Energy Communities: A Multiagent System Approachs challenges to the existing grid as it was not designed to have reverse power flows. Local energy communities are effective in addressing those issues and engaging grid users to play an active role in the energy transition. Such communities encourage the consumption of the excess of renewable energ
48#
發(fā)表于 2025-3-29 20:18:45 | 只看該作者
A Game-Theoretical Incentive Mechanism for Local Energy Communitiessers participating in the community, which are able to produce energy rather than just consuming it. These communities have the purpose to incentivize usage of renewable energy. Inside them, it is possible to have members that trade energy in a peer-to-peer (P2P) fashion: prosumer can trade their en
49#
發(fā)表于 2025-3-30 01:31:29 | 只看該作者
50#
發(fā)表于 2025-3-30 04:10:25 | 只看該作者
Cooperative Multi-agent Systems for the Multi-target ,-Coverage ProblemTo allow for scalability and robustness, we propose a novel distributed approach performed by autonomous robots based on their willingness to interact with each other. This willingness, based on their individual state, is used to inform a decision process of whether or not to interact with other rob
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