Titlebook: KI 2024: Advances in Artificial Intelligence; 47th German Conferen Andreas Hotho,Sebastian Rudolph Conference proceedings 2024 The Editor(s

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爭吵加

斑駁

Could the?Declarer Have Discarded It? Refined Anticipation of?Cards in?Skatle worlds, we additionally use the search tree size and depth to prefer short proofs. In hundreds of thousands human ouvert games replayed by our AIs over 99% matched the predictions of the open-card solver, with only 0.21% of games known lost for the declarer were not won by the AIs, both trademarks outperforming human play.

prediabetes

A Framework for?General Trick-Taking Card Gamesding, and game selection, as well as general and specialized card recommenders applicable for the different stages of trick-taking. We study the impact of expert rules for enhanced play. The AIs are evaluated in different variants and against a general card player that lacks expert rules.

弄污

Image Dataset Quality Assessment Through Descriptive Out-of-Distribution Detectioning desired data as well as undesired data. Notably, this method does not require the training of a machine learning model. In addition, this work presents a new image dataset suited for evaluating data cleaning tasks in a way that has practical relevance, and demonstrates satisfactory experimental results.

Lipohypertrophy

Evaluating AI-Based Components in?Autonomous Railway Systems, formal verification techniques, and real-time monitoring. By leveraging these methods, we provide a comprehensive safety assurance and hopefully pave the way for the widespread adoption of AI in railway transportation systems.

Iniquitous

Conference proceedings 2024e for exchanging news?and research results on theory and applications.?The papers have been categorized into the following sections: full technical papers;? technical communications; extended abstracts of papers from other AI conferences..

cataract

0302-9743 ideal place for exchanging news?and research results on theory and applications.?The papers have been categorized into the following sections: full technical papers;? technical communications; extended abstracts of papers from other AI conferences..978-3-031-70892-3978-3-031-70893-0Series ISSN 0302-9743 Series E-ISSN 1611-3349

概觀

Data Augmentation in?Latent Space with?Variational Autoencoder and?Pretrained Image Model for?Visualrather than directly manipulating pixel values. This method utilizes a Variational Autoen- coder, integrated with a pretrained image model, to facilitate the data augmentation process in a more abstract and feature-rich latent space. We use the DeepMind Control suite as a benchmark to evaluate the impact of our approach.

integral

A Note on Linear Time Series Predictionn a unified way, we can show how PCA-based time series prediction can be categorized in different settings of stochastic and deterministic models. Finally, we show the distinct relation between PCA-based prediction and (finite-order) MA processes and propose a refined methodology.