This book constitutes selected papers from the refereed proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, which was held online during February 4-6, 2021.
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.
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Illustrationen
89 farbige Abbildungen, 54 s/w Abbildungen
XIV, 341 p. 143 illus., 89 illus. in color.
ISBN-13
978-3-031-10161-8 (9783031101618)
DOI
10.1007/978-3-031-10161-8
Schweitzer Klassifikation
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.
