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Titlebook: Nursing: Health Education and Improving Patient Self-Management; Barbara Sassen Book 20181st edition Springer International Publishing AG

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11#
發(fā)表于 2025-3-23 12:38:22 | 只看該作者
Barbara SassenS, and details the cooperative behaviors of the two types of agents we defined for ontology evolution. Finally evaluations made on three different ontologies are given in order to show the genericity of our solution.
12#
發(fā)表于 2025-3-23 14:02:48 | 只看該作者
Barbara Sassenn consecutive days are strongly correlated, and (iii) there exists a trade-off between the frequency of decision making and more complex decision criteria, on one side, and the negative outcome of lost trading on the agents’ side due to them not participating actively in the market for some of the execution steps.
13#
發(fā)表于 2025-3-23 21:21:16 | 只看該作者
14#
發(fā)表于 2025-3-24 01:00:23 | 只看該作者
is problem is a heuristic algorithm which determines (locates in the corresponding space) feasible probability values to be used so as to obtain the maximum average score. This project has also involved the corresponding implementation of the game, and the output of the new algorithm enables the user to visualize the details.
15#
發(fā)表于 2025-3-24 05:56:41 | 只看該作者
Barbara Sassennd with little delay. Here, we show how this can be achieved by distributing the k-Nearest Neighbors machine learning algorithm over MPI. We hope this would motivate the research into other combinations of merging machine learning algorithms with Vehicular Data sets.
16#
發(fā)表于 2025-3-24 07:34:59 | 只看該作者
17#
發(fā)表于 2025-3-24 13:45:16 | 只看該作者
18#
發(fā)表于 2025-3-24 18:38:43 | 只看該作者
19#
發(fā)表于 2025-3-24 21:54:31 | 只看該作者
Barbara Sassenny studies have focused on the analysis of adaptive learning agents carrying on prices. But the prices are a consequence of the matching orders. Reasoning about orders should help to anticipate future prices..While it is easy to populate these virtual worlds with agents analyzing “simple” prices sha
20#
發(fā)表于 2025-3-25 00:18:41 | 只看該作者
Barbara Sassenations. The construction and evolution of an ontology are complex and time-consuming tasks. This paper presents DYNAMO-MAS, an Adaptive Multi-Agent System (AMAS) that automates these tasks by co-constructing an ontology from texts with an ontologist. Terms and concepts of a given domain are agentifi
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