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

ISBN-13: 9783031101601 / Angielski / Miękka / 2022 / 341 str.

Ana Paula Rocha;Luc Steels;Jaap van den Herik

Opis

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 artificial intelligence.

Spis treści

​Agents.- Specification 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 Workflow Enactment.- Negotiation Considering Privacy Loss on Asymmetric Multi-objective Decentralized Constraint Optimization Problem.- Artificial 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 efficient Transformer Architecture using Convolutional Layers.- Improving the Generalization of Deep Learning Classification Models in Medical Imaging using Transfer Learning and Generative Adversarial Networks.- An Interpretable Word Sense Classifier 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 Inflation within Virtual Economies using Deep Reinforcement Learning.- Practical City Scale Stochastic Path Planning with Pre-computation.