Titlebook: Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference; Kenji Matsui,Sigeru Omatu,Sara Rodríguez Gonzá

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加花粗鄙人

Justyna Maliszewska-Nienartowiczed how theorem proving can be applied to verify agent systems. We formalize a framework for verification of cognitive agent programs in a proof assistant. This enables access to powerful automation and provides assurance that our results are correct.

PARA

A Theorem Proving Approach to Formal Verification of a Cognitive Agent,ed how theorem proving can be applied to verify agent systems. We formalize a framework for verification of cognitive agent programs in a proof assistant. This enables access to powerful automation and provides assurance that our results are correct.

宣传

2367-3370 ntries, representing a truly “wide area network” of research activity. The DCAI’21 technical program has selected 21 papers, and, as in past editions, it will be special issues in ranked journals such as Electr978-3-030-86260-2978-3-030-86261-9Series ISSN 2367-3370 Series E-ISSN 2367-3389

眼界

Utilising Data from Multiple Production Lines for Predictive Deep Learning Models,ork layer finds an internal representation of each individual BOF, while the other layers use this representation to concurrently learn a common BOF model. Our evaluation shows that the prediction accuracy of the common model increases compared to separate models trained on individual furnaces’ data

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表皮

镇压

Wind Speed Forecasting Using Feed-Forward Artificial Neural Network,forecast wind speed. The proposed model is applied to real data from a wind farm in the south of South America. Results show that the proposed model can achieve lower forecasting errors than the baseline models, which consist of Numerical Weather Predictions.

护航舰

Service-Oriented Architecture for Data-Driven Fault Detection,whereby the isolation forest algorithm is used to detect potentially anomalous data points and the anomaly threshold is defined by computing the interquartile range of anomaly scores. To compensate for the fact that single anomalies are unlikely to be of significance, when deploying the isolation fo

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