Buch, Englisch, Band 13251, 341 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 546 g
13th International Conference, ICAART 2021, Virtual Event, February 4-6, 2021, Revised Selected Papers
Buch, Englisch, Band 13251, 341 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 546 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-031-10160-1
Verlag: Springer International Publishing
A total of 72 full and 99 short papers were carefully reviewed and selected for the conference from a total of 298 submissions; 17 selected full papers are included in this book. They were organized in topical sections named agents and arti?cial intelligence.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Agents.- Speci?cation Aware Multi-Agent Reinforcement Learning.- Task Bundle Delegation for Reducing the Flowtime.- A Detailed Analysis of a Systematic Review about Requirements Engineering Processes for Multi-Agent Systems.- Automatically-generated Agent Organizations for Flexible Work?ow Enactment.- Negotiation Considering Privacy Loss on Asymmetric Multi-objective Decentralized Constraint Optimization Problem.- Arti?cial Intelligence.- Utilizing Out-domain Datasets to Enhance Multi-task Citation Analysis.- Using Possibilistic Networks to Compute Learning Course Indicators.- Assured Deep Multi-Agent Reinforcement Learning for Safe Robotic Systems.- How to Segment Handwritten Historical Chronicles using Fully Convolutional Networks?.- On the Relationship with Toulmin Method to Logic-based Argumentation.- Informer: An e?cient Transformer Architecture using Convolutional Layers.- Improving the Generalization of Deep Learning Classi?cation Models in Medical Imaging using Transfer Learning and Generative Adversarial Networks.- An Interpretable Word Sense Classi?er for Human Explainable Chatbot.- A Tsetlin Machine Framework for Universal Outlier and Novelty Detection.- Adding Supply/Demand Imbalance-sensitivity to Simple Automated Trader-agents.- Advances in Measuring In?ation within Virtual Economies using Deep Reinforcement Learning.- Practical City Scale Stochastic Path Planning with Pre-computation.
