Titlebook: Artificial Intelligence in Manufacturing; Enabling Intelligent John Soldatos Book‘‘‘‘‘‘‘‘ 2024 The Editor(s) (if applicable) and The Author

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Reinforcement Learning-Based Approaches in Manufacturing Environmentss is particularly evident in manufacturing industries, where the training phase of a reinforcement learning agent is constrained, resulting in suboptimal performance of developed strategies. To address this challenge, digital environments are typically created, allowing agents to freely explore the

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I4.0 Holonic Multi-agent Testbed Enabling Shared Productione goal of the modular architecture is to enable flexible, resilient and distributed production. The presented approach illustrates how Multi-Agent Systems (MAS) can be incorporated in the manufacturing domain for distributed components on different hierarchy layers based on a holonic approach. The c

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A Multi-intelligent Agent Solution in the Automotive Component–Manufacturing Industryials received from suppliers differ from one another, though within tolerances. And similar variations appear in all areas of manufacturing, such as tool wearing, the statuses of production machines, and even operator decisions..Improvements to production must factor in technical considerations and

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Integrating Knowledge into Conversational Agents for Worker Upskillingitations, however, is that obtaining the best opportunity can be hard because certain jobs need some experiences, abilities, and features that an applicant might not know. The recent diffusion and employment of conversational agents definitely have proven to benefit this kind of issue. For example,

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Tremor

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La montatura equatoriale dei telescopi, decide the veracity of the learned knowledge with respect to the given context. It enables humans to collaborate with AI systems through intuitive interfaces that help domain experts in interpreting insights, validating the findings, and applying domain knowledge to gain a deeper understanding of t

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