| 1. |
- Antel, C., et al.
(författare)
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Feebly-interacting particles : FIPs 2022 Workshop Report
- 2023
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Ingår i: European Physical Journal C. - : Springer. - 1434-6044 .- 1434-6052. ; 83:12
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Forskningsöversikt (refereegranskat)abstract
- Particle physics today faces the challenge of explaining the mystery of dark matter, the origin of matter over anti-matter in the Universe, the origin of the neutrino masses, the apparent fine-tuning of the electro-weak scale, and many other aspects of fundamental physics. Perhaps the most striking frontier to emerge in the search for answers involves new physics at mass scales comparable to familiar matter, below the GeV-scale, or even radically below, down to sub-eV scales, and with very feeble interaction strength. New theoretical ideas to address dark matter and other fundamental questions predict such feebly interacting particles (FIPs) at these scales, and indeed, existing data provide numerous hints for such possibility. A vibrant experimental program to discover such physics is under way, guided by a systematic theoretical approach firmly grounded on the underlying principles of the Standard Model. This document represents the report of the FIPs 2022 workshop, held at CERN between the 17 and 21 October 2022 and aims to give an overview of these efforts, their motivations, and the decadal goals that animate the community involved in the search for FIPs.
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| 2. |
- Dragone, Mauro, et al.
(författare)
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A cognitive robotic ecology approach to self-configuring and evolving AAL systems
- 2015
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Ingår i: Engineering applications of artificial intelligence. - : Pergamon-Elsevier Science. - 0952-1976 .- 1873-6769. ; 45, s. 269-280
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Tidskriftsartikel (refereegranskat)abstract
- Robotic ecologies are systems made out of several robotic devices, including mobile robots, wireless sensors and effectors embedded in everyday environments, where they cooperate to achieve complex tasks. This paper demonstrates how endowing robotic ecologies with information processing algorithms such as perception, learning, planning, and novelty detection can make these systems able to deliver modular, flexible, manageable and dependable Ambient Assisted Living (AAL) solutions. Specifically, we show how the integrated and self-organising cognitive solutions implemented within the EU project RUBICON (Robotic UBIquitous Cognitive Network) can reduce the need of costly pre-programming and maintenance of robotic ecologies. We illustrate how these solutions can be harnessed to (i) deliver a range of assistive services by coordinating the sensing & acting capabilities of heterogeneous devices, (ii) adapt and tune the overall behaviour of the ecology to the preferences and behaviour of its inhabitants, and also (iii) deal with novel events, due to the occurrence of new user's activities and changing user's habits.
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| 3. |
- Dragone, Mauro, et al.
(författare)
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A Programming Framework for Multi-agent Coordination of Robotic Ecologies
- 2013
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Ingår i: Programming Multi-Agent Systems. - Berlin, Heidelberg : Springer Publishing Company. - 9783642387005 ; , s. 72-89
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Konferensbidrag (refereegranskat)abstract
- Building smart environments with Robotic ecologies, comprising of distributed sensors, actuators and mobile robot devices facilitates and extends the nature and form of smart environments that can be developed, and reduces the complexity and cost of such solutions. While the potentials of such an approach makes robotic ecologies increasingly popular, many fundamental research questions remain open. One such question is how to make a robotic ecology self-adaptive, so as to adapt to changing conditions and evolving requirements, and consequently reduce the amount of preparation and pre-programming required for their deployment in real world applications. This paper presents a framework for the specification and the programming of robotic ecologies. The framework extends an existing agent system and integrates it with the pre-existing and dominant traditional robotic and middleware approach to the development of robotic ecologies. We illustrate how these technologies complement each other and offer a candidate technology to pursue adaptive robotic ecologies.
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