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Titlebook: Applications and Innovations in Intelligent Systems XIV; Proceedings of AI-20 Richard Ellis,Tony Allen,Andrew Tuson Conference proceedings

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樓主: 清楚明確
31#
發(fā)表于 2025-3-27 00:47:00 | 只看該作者
https://doi.org/10.1007/978-1-84628-666-7Bayesian network; Simulation; agents; artificial intelligence; data mining; genetic algorithms; grammar; na
32#
發(fā)表于 2025-3-27 02:59:04 | 只看該作者
33#
發(fā)表于 2025-3-27 07:43:59 | 只看該作者
34#
發(fā)表于 2025-3-27 12:51:13 | 只看該作者
Managing Restaurant Tables using Constraintscombinatorial problem. We show how to model the problem as constraint satisfaction, with extensions which generate flexible seating plans and which maintain stability when changes occur. We describe an implemented system which provides advice to users in real time. The system is currently being evaluated in a restaurant environment.
35#
發(fā)表于 2025-3-27 15:27:52 | 只看該作者
Use of Data Mining Techniques to Model Crime Scene Investigator Performance can be placed in one of four groups according to their ability to recover DNA and fingerprints from crime scenes. They also show that their ability to predict which crime scenes will yield the best opportunity of recovering forensic samples has no correlation to their actual ability to recover those samples.
36#
發(fā)表于 2025-3-27 18:55:05 | 只看該作者
https://doi.org/10.1007/978-981-99-4004-2combinatorial problem. We show how to model the problem as constraint satisfaction, with extensions which generate flexible seating plans and which maintain stability when changes occur. We describe an implemented system which provides advice to users in real time. The system is currently being eval
37#
發(fā)表于 2025-3-28 00:14:18 | 只看該作者
https://doi.org/10.1007/978-3-030-55165-0 can be placed in one of four groups according to their ability to recover DNA and fingerprints from crime scenes. They also show that their ability to predict which crime scenes will yield the best opportunity of recovering forensic samples has no correlation to their actual ability to recover thos
38#
發(fā)表于 2025-3-28 02:19:49 | 只看該作者
https://doi.org/10.1007/978-3-030-55165-0rative Learning (CSCL) systems are used to supportcollaborative learning and knowledge building, making communication tools, shared workspaces, and automatic analysis tools available to users. In this article we describe a Bayesian network automatically built from a database of analysis indicators q
39#
發(fā)表于 2025-3-28 10:10:29 | 只看該作者
40#
發(fā)表于 2025-3-28 14:12:37 | 只看該作者
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