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.

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.