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Titlebook: Agents and Artificial Intelligence; 8th International Co Jaap van den Herik,Joaquim Filipe Conference proceedings 2017 Springer Internation

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Cultural Studies‘ Affective Voicessed recommender system that takes into account user preferences to generate a plan satisfying those preferences. Agents in this framework have a Belief-Desire-Intention (BDI) component based on the well-known BDI architecture. These BDI agents are empowered with cognitive capabilities in order to in
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Cultural Studies‘ Affective Voices naturally subject to uncertainties. Robust proactive planning of day-ahead real power provision must incorporate uncertainty in feasibility when trading off different schedules against each other during the predictive planning phase. Deviations from the expected initial operational state of an ener
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https://doi.org/10.1007/978-981-19-3915-0due to its potential in processing information originating from the Web in innovative ways. In this paper, we propose to apply the argument mining pipeline to the text exploration task. First, starting from the arguments put forward in online debates, we introduce . to predict the relation among the
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Cultural Sustainability and Arts Educations been proposed. The cascade classifier tackles high-dimensionality and imbalance by splitting the classification task into several low-dimensional classification tasks and aggregating the intermediate results. Therefore the high-dimensional data set is projected onto low-dimensional subsets. But th
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Cultural Sustainability and Arts Education expressed on the same structure by using ordinal data. In many applications, it may be natural to represent expert knowledge and preferences separately and treat all nodes similarly. This work shows how an influence diagram can be equivalently represented by two possibilistic networks: the first on
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Christoph Richter,Heidrun Allert dimensionality. Clustering algorithms are useful in analyzing these large datasets but often fall short to provide completely satisfactory results. Integrating clustering and visualization not only yields better clustering results but also leads to a higher degree of confidence in the findings. Sel
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Rebecca L. Kauten,Olivia J. Calvin consequences of ABox (.) and TBox (.) denoted by . and . respectively. A secrecy set of a querying agent is subset . of . which the agent is not allowed to access. Next we compute envelopes which provide logical protection to the secrecy set against the reasoning of the querying agent. Once envelop
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