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Titlebook: Agents and Artificial Intelligence; Third International Joaquim Filipe,Ana Fred Conference proceedings 2013 Springer-Verlag Berlin Heidelb

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Symbolic State-Space Exploration and Guard Generation in Supervisory Control Theorynt such that given specifications is fulfilled. The main obstacle which prevents SCT from having a major industrial breakthrough is that the supervisory synthesis, consisting of a series of reachability tasks, suffers from the state-space explosion problem. To alleviate this problem, a well-known st
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Developing Goal-Oriented Normative Agents: The NBDI Architecture to do and by stating stimulus to their fulfillment such as rewards and discouraging their violation by pointing out punishments. In this paper we propose the NBDI architecture to develop goal-oriented normative agents whose priority is the accomplishment of their own desires while evaluate the pros
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Evaluation of Environment Contextual Services in Multiagent Systems environment and the effects of their actions are observed and evaluated through this environment. The local complexity of the agents depends on its management of the interaction and action processes. If the environment carries out a part of this processes, the complexity of the agents is reduced. T
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Data Streams Classification: A Selective Ensemble with Adaptive Behaviorsmartphones and sensor networks, related to communication services introduces new challenges in the analysis of streaming data. The latter requires the use of approaches that require little time and space to process a single item, providing an accurate representation of only relevant data characteri
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Web Service Composition Plans in OWL-Sf complex tasks. One of the approaches used in the past in order to automatically construct composite web services has been AI planning. The most important advantage of this approach is its dynamic character that reduces the interference of the user. Although there have been various attempts to util
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Cultural Work and Higher Education. We do . focus on how well the measured cross-validation outcome estimates the generalization error or how far it is from the training error; instead, we want to make statements about the cross-validation result without actually measuring it. In particular we want to state probabilistically what .-
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