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E-raamat: Agents and Artificial Intelligence: 13th International Conference, ICAART 2021, Virtual Event, February 4-6, 2021, Revised Selected Papers

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Agents and Artificial Intelligence: 13th International Conference, ICAART 2021, Virtual Event, February 4-6, 2021, Revised Selected PapersSuurem pilt
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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.
Agents.- Specication 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 Workow
Enactment.- Negotiation Considering Privacy Loss on Asymmetric
Multi-objective Decentralized Constraint Optimization Problem.- Articial
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 ecient Transformer Architecture
using Convolutional Layers.- Improving the Generalization of Deep Learning
Classication Models in Medical Imaging using Transfer Learning and
Generative Adversarial Networks.- An Interpretable Word Sense Classier 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 Ination within Virtual
Economies using Deep Reinforcement Learning.- Practical City Scale Stochastic
Path Planning with Pre-computation.
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