The human race is facing one of the most meaningful public health emergencies in the modern era caused by the COVID-19 pandemic. This pandemic introduced various challenges, from lock-downs with significant economic costs to fundamentally altering the way of life for many people around the world. The battle to understand and control the virus is still at its early stages yet meaningful insights have already been made. The uncertainty of why some patients are infected and experience severe symptoms, while others are infected but asymptomatic, and others are not infected at all, makes managing this pandemic very challenging. Furthermore, the development of treatments and vaccines relies on knowledge generated from an ever evolving and expanding information space. Given the availability of digital data in the modern era, artificial intelligence (AI) is a meaningful tool for addressing the various challenges introduced by this unexpected pandemic. Some of the challenges include: outbreak prediction, risk modeling including infection and symptom development, testing strategy optimization, drug development, treatment repurposing, vaccine development, and others.

The Thirty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS-34) was to be held May 17-19, 2021, at the Double Tree Ocean Point Resort and Spa in North Miami Beach, Florida, USA. Due to COVID-19 pandemic and travel restriction, the conference held both virtual and in-person. The planned conference events included tutorials, invited speakers, special tracks, and presentations of papers, posters, and awards. The conference chair was Keith Brawner from the Army Research Laboratory. The program co-chairs were Roman Barták from Charles University, Prague, and Eric Bell, USA.

The Symposium on Educational Advances in Artificial Intelligence (EAAI) seeks to advance the AAAI goal of improving the teaching and training of AI practitioners. Organized as an independent symposium within the AAAI conference, EAAI provides the opportunity for researchers, educators, and students to share educational experiences involving AI. EAAI 2021 provided a venue for discussing pedagogical issues and sharing resources related to teaching and using AI in education. It cut across a variety of curricular levels, from K-12 through postgraduate training. The symposium showcased ideas for how to effectively teach AI as well as the growing impact of AI in enhancing education.

The Thirty-First International Florida Artificial Intelligence Research Society Conference (FLAIRS-31) was held May 21-23, 2018, at the Crowne Plaza Oceanfront in Melbourne, Florida, USA. The conference events included invited speakers, special tracks, and presentations of papers, posters, and awards. The conference chair was Zdravko Markov from Central Connecticut State University. The program co-chairs were Vasile Rus from the University of Memphis and Keith Brawner from the Army Research Laboratory. The special tracks were coordinated by Roman Barták from Charles University in Prague.

One consistent and growing area of concentration of artificial intelligence is in the area of games — serious games and simulations, educational games, and traditional game AI — and in multimedia — the interaction of logic and reasoning within the realm of media. Within these contexts, the goal is the same — simulating intelligent agents that will react strategically to player behaviors and the environment. Improvements and advancements within this field will lead to increased veracity of simulations, enhanced learning within educational games, and more realistic and complicated gameplay. Additionally, advances in AI in games and media are worthy of study. This opens up the study to the area of multimedia — how are we using AI to shape the future of multimedia?

Healthcare informatics focuses on the efficient and effective acquisition, management, and use of information in healthcare. Advancing health informatics has been declared a grand challenge by the National Academy of Engineering and is a major area of emphasis for agencies such as the Centers for Medicare and Medicaid Services. As such, it has been identified as an area of national need. Sample uses of AI in health informatics includes expert systems for decision support, machine learning and data mining to discover patterns across patients, image analysis to assist in diagnosis, and natural language processing to extract information from free text medical documents. The areas of interest for this track include healthcare decision support, medical image processing, machine learning and data mining in healthcare, processing and managing patient records, syndromic surveillance, drug discovery, and personalization of clinical care.

Intelligent learning technologies (ILT) include a diverse array of computer-based systems and tools designed to foster meaningful student learning. These technologies are intelligent to the extent they implement artificial intelligence principles and techniques to create teachable structure from content, analyze and model inputs from the learner, and generate personalized and adaptive feedback and guidance. Intelligent tutoring systems (ITSs) represent a classic example. ITSs, broadly defined, possess an outer loop that intelligently selects the next relevant task, or content object, for learners to complete based on prior performance, and an inner loop that provides iterative and intelligent feedback as learners work toward completing their tasks. However, intelligent learning technologies encompass more than just intelligent tutors. Increasingly, educational games, automated writing evaluation, virtual pedagogical agents, simulations, virtual worlds, open-ended problem solving, generative concept maps, AI-assisted authoring systems, learning content aggregation programs, and e-textbooks rely on some form of artificial intelligence to enrich the learning experience.

Recommender systems are being used to suggest products to customers, provide personalized product information, and even to provide product reviews. ese systems recommend items among a huge number of possibilities according to users' interests. Recommender systems have al- so been proposed to support the information selection and decision-making processes on e-com- merce web sites. is is the fourth recommender systems special track running in parallel with FLAIRS. The goal of this new special track has been to provide a forum for researchers and practitioners to share their e orts in addressing current issues, challenges, novel approaches, and applica- tions within the broad scope of recommender systems. We continue to aim to cover a wide variety of research areas where recommender systems may be researched and applied.

The track on applied natural language processing is a forum for researchers working in natural language processing, computational linguistics, and related areas. The rapid pace of development of online materials, most of them in textual form or text combined with other media (visual, audio), has led to a revived interest for tools capable to understand, organize and mine those materials. Novel human-computer interfaces, for instance talking heads, can benefit from language understanding and generation techniques with big impact on user satisfaction. Moreover, language can facilitate human-computer interaction for the handicapped (no typing needed) and elderly leading to an ever increasing user base for computer systems.

This track includes data-related tasks such as analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy, with special focus on social data on the web. Hence, the broader context of the track comprehends AI, web mining, information retrieval, natural language processing, and sentiment analysis. As the web rapidly evolves, web users are evolving with it. In an era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the social web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely difficult task, as the contents of today’s web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction. The primary aim of this track is exploring the new frontiers of big data computing for opinion mining and sentiment analysis through machine learning techniques, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the web.