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Titlebook: Agents and Robots for reliable Engineered Autonomy; 4th Workshop, AREA 2 Angelo Ferrando,Rafael C. Cardoso Conference proceedings 2025 The

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樓主: adulation
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發(fā)表于 2025-3-26 22:03:12 | 只看該作者
32#
發(fā)表于 2025-3-27 04:42:02 | 只看該作者
33#
發(fā)表于 2025-3-27 08:11:42 | 只看該作者
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發(fā)表于 2025-3-27 12:51:31 | 只看該作者
978-3-031-73179-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
35#
發(fā)表于 2025-3-27 14:11:06 | 只看該作者
36#
發(fā)表于 2025-3-27 20:24:29 | 只看該作者
Guide to Graphics Software Toolselopment. Like any complex software, robotic software development requires disciplined processes to ensure efficiency and robustness. Despite the potent capabilities of ROS and Gazebo, current methodologies for developing robotic systems often omit a dedicated design phase, leading to the absence of
37#
發(fā)表于 2025-3-28 00:42:54 | 只看該作者
Web3D Tools and Networked Environmenthysical surroundings through sensors and make impactful decisions that influence the environment. Software engineering challenges in this domain include the specification of interactive multi-agent tasks. The general-purpose Domain-Specific Language named LIrAs, Language for Interactive Agents, is a
38#
發(fā)表于 2025-3-28 02:54:23 | 只看該作者
Springer Professional Computing by confined spaces, limited visibility, and strict navigation requirements. The research focuses on integrating human-controlled vehicles into coordinated AV fleets, addressing the unpredictable interactions that arise from human behaviour. The ORU coordination framework, originally designed for a
39#
發(fā)表于 2025-3-28 08:05:16 | 只看該作者
Guide to Graphics Software Tools on a mobile robot. Traditionally effective in dense, high-dimensional domains like image processing, VAEs face unique challenges when adapted to sparse, low-dimensional sensory data. This paper contributes to broader efforts to tailor deep generative models to the complexities of robotic sensory da
40#
發(fā)表于 2025-3-28 10:53:28 | 只看該作者
Guide to Graphics Software Toolsipeline transforming the initial specification of the non-deterministic planning problem into an AND-OR tree, which is then used to solve for non-deterministic post conditions and generate a contingency plan in AgentSpeak.
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