Special Tracks

Special tracks are held in parallel with the general conference and are an integral part of the conference. They provide researchers in focused areas the opportunity to meet and present their work, and offer a forum for interaction among the broader community of artificial intelligence researchers. Special track papers are required to meet the same standards as papers in the general conference and are published in the same conference proceedings.

The following special tracks are offered at FLAIRS 2018:
Submissions to special tracks follow the same rules and deadlines as for the general conference track.

Special Track's coordinator: Roman Barták, Charles University, Czech Republic (bartak@ktiml.mff.cuni.cz)

Data Mining [web page]

Data mining is the process of extracting hidden patterns from data. As more data is gathered, data mining is becoming an increasingly important tool to transform this data into information. It is commonly used in a wide range of profiling services, such as marketing, surveillance, fraud detection and scientific discovery.

Track chairs: David Bisant, William Eberle

Uncertain Reasoning [web page]

The topics of this special track include all issues related to reasoning under uncertainty.

Track chairs: Karim Tabia, Mohand Said Allili

Case-Based Reasoning [web page

Case-Based Reasoning (CBR) is an Artificial Intelligence problem solving and analysis methodology that retrieves and adapts previous experiences to fit new contexts. This forum is intended to gather AI researchers and practitioners with an interest in CBR to present and discuss developments in CBR theory and application.

Track chairs: Stelios Kapetanakis, Michael W. Floyd

Recommender Systems [web page]

Recommender systems are being used to suggest products to customers, provide personalized product information, or even to provide products' reviews. These systems recommend items among a huge number of possibilities and according to users' interests. Recommender Systems have also been proposed to support the information selection and decision making processes on e-commerce web sites. 

Track chairs: Nadia Najjar, Yong Zheng, Carlos E. Seminario 

Autonomous Robots and Agents [web page]

Robotics and Artificial Intelligence are closely related areas though their research interests and topics diverted in past. Recently, the progress in both areas brings robotics and artificial intelligence together again and higher-level deliberative functions such as action planning are being integrated into usually reactive robotics systems to increase their autonomy as well as to simplify their control. The special track addresses research results on the border between robotics (and general intelligent agents) and AI techniques with the aim to bridge the enlarging gap between the areas.

Track chairs: Roman Barták, David Obdržálek

Applied Natural Language Processing [web page]

The track on Applied Natural Language Processing is a forum for researchers working in natural language processing (NLP)/computational linguistics(CL) 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.

Track chairs: Fazel Keshtkar, Chutima Boonthum-Denecke  

Semantic, Logics, Information Extraction and AI [web page]

This track is intended to present works ranking from logical, mathematical, and statistical models in syntax and semantics (logic of objects, topological theories of time and space, lexical associations, etc.) as foundations of the design and analysis to knowledge processing and natural language processing systems and especially to information extraction. 

Track Chairs: Ismail Biskri, Anca Pascu, Vladislav Kuboň

AI in Games, Serious Games, and Multimedia [web page]

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?

Track chairs: Michael Franklin, Cedric Buche

AI in Healthcare Informatics [web page]

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 & 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. 

Track chairs: Doug Talbert, Steve Talbert

AI for Big Social Data Analysis [web page]

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.

Track chairs: Eric Bell, Viviana Patti

AI for Digital Humanities [web page]

Digital humanities is a newly emerging field that brings together humanities scholars, social scientists and computer and information scientists to work on both fundamental and applied research in humanities. As techniques in Artificial Intelligence/Machine Learning and Data Mining have matured there is a wide range of computational tools, methods, and techniques have enabled humanities scholars to conduct research at a scale once thought impossible. This special track is calling for the submission of novel research results demonstrating the success and chanllenges of applying Artificial Intelligence techniques in digital humanities research, such as data discovery, digital data creation, management, data analytics in literatures, linguistics, culture heritage, media, social science and history. This special track aims not only to serve a venue for presenting work in this area, but also to build a community and share information in this area. 

Track chairs: Yudong Liu, James Hearne 

Intelligent Support for Decision Making in the Context of Mobility [web page]

Decision making support is an important application of computers, as intelligent techniques are needed to help humans with managing and processing the large and complex quantities of data that can influence their thinking. The involved techniques are at the confluence of automated reasoning, planning, communication, learning, data mining, and human computer interaction. For example, in the design of semi-autonomous cars where the system is supposed to assist a human in charge, the challenges of supporting humans while giving them the right sense of confidence are apparent. In many applications, it is considered that the humans still want to feel that they are in charge and controlling the overall behavior of the system, while a lot of subtasks are undertaken by intelligent mechanisms. This happens not only in physical but also in social systems, where stability of social networks has significant challenges. 

Track chairs: Marius Silaghi, Markus Zanker, Rene Mandiau

Intelligent Learning Technologies [web page]

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.

Track chairs: Benjamin Nye, Stephen E. Fancsali

Human-agent Teamwork for Cyber Operations [web page]

Security events occur in such volume and at such a high rate that it is impractical to envision human operators handling all of them alone. Progress in artificial intelligence techniques such as Bayesian modeling, game theory, behavior modeling, machine learning and plan recognition have all had a significant impact on the capabilities of cyber defence systems. However, these systems often operate without regard to context or changing operational requirements. Teamwork is a compelling metaphor that can be used to describe the interaction of human operators with theses advanced detection, modeling and actuation techniques. This track focuses on techniques, issues, and implications of human-agent teamwork as applied to cyber operations. 

Track chairs: Thomas C Eskridge, Marco M Carvalho

Applications of AI in Business and Industry [web page]

AI applications in industry and business present unique challenges. Datasets are incomplete, inconsistent, noisy, or of poor quality. AI models have to incorporate domain-specific knowledge and are constrained by business demands. Several design compromises are required to effectively leverage AI techniques. Applying well-understood AI algorithms to new problems reveals new limitations of the algorithms and leads to better understanding of the algorithms. It also enables better-designed and newer algorithms. Techniques developed by industry researchers to meet these challenges generalise into AI best practices for real world problems. This track provides a venue for discussing these unique challenges

Track chairs: Chayan Chakrabarti, Saurabh Thapliyal