At the moment the following special sessions have been confirmed:
- In Memory of Naftali Tishby
- Boolean and pseudo-Boolean Functions
- Coalition Formation Games
- Combinatorial and Geometric Problems in Imaging Sciences
- Formalization in Mathematics
- Sequencing, Sequential Decision Making and Scheduling
- Uncertain Reasoning
In Memory of Naftali Tishby
Organizer: Martin Golumbic
Naftali Tishby, professor of computer science and computational neuroscience at the Hebrew University of Jerusalem, passed away on August 9, 2021 at the age of 68. He was an invited plenary speaker at ISAIM-2008 lecturing on Extracting Relevant Information from Samples. In this special session, several of his coauthors and former students will give invited lectures on their current research in his memory.
Boolean and pseudo-Boolean Functions
Organizers: Endre Boros and Yves Crama
Boolean and pseudo-Boolean functions are pervasive today in all areas of mathematics, computer science, operations research, various sciences and engineering. An ever increasing number and areas of applications demand new results from both structural and algorithmic points of views. The special sessions aim at bringing together researchers from all walks of science to discuss the latest results and the most important open problems.
Coalition Formation Games
Organizers: Judy Goldsmith and Jörg Rothe
Many AI applications concern settings in which individual agents choose to act as a group, or coalition. Such scenarios can be modeled as cooperative games, including hedonic games, coalition formation games, weighted voting games, and many more. The aim of this special session is to bring together different communities working in cooperative games from various perspectives in computer science and economics and to bridge and bundle their research activities.
Combinatorial and Geometric Problems in Imaging Sciences
Organizers: Valentin Brimkov
Artificial Intelligence approaches and technologies are germane to various imaging science disciplines, including image analysis, image understanding, computer vision, medical imaging, and biometrics. These are in turn applicable to societally sensitive areas like medicine, defense, and security. The ultimate goal regarding possible applications is the design of intelligent computer systems capable of solving intricate practical problems. To this end, researchers often need to cope with challenging mathematical problems. This special session aims to present results on some combinatorial and geometric problems of this kind and to provoke a discussion on possible applications.
Formalization in Mathematics
Organizers: Gonzalo A. Aranda-Corral and Francisco Jesús Martín Mateos
Nowadays, there are a lot of processes developed by autonomous systems. These systems are designed to follow some kind of algorithms with a mathematical basis. The key to ensure the correctness of these processes is the formalization of underlying mathematical theory.
Therefore, Formalization of mathematics can play a very important role in many areas (theoretical and applied). From the statement of simple theories to the verification of correctness for properties in complex algorithms and systems, formalization of mathematics can be the main tool used to increase our confidence in the results. This special session aims to bring researchers the opportunity to discuss latest results and research challenges in this and close related areas.
Sequencing, Sequential Decision Making and Scheduling
Organizers: Lisa Hellerstein and Thomas Lidbetter
Sequencing problems are important in many areas of artificial intelligence and operations research. They address problems such as how to optimally schedule a set of jobs or tests or how to optimally search for a hidden target. This special session aims to bring together researchers from different communities to discuss recent progress and address new challenges in this area.
Organizers: Alessandro Antonucci, Salem Benferhat, and Kamal Premaratne
Many applications are increasingly being challenged with how to deal with imperfect data of various kinds. One problem that often arises with imperfect data is that of managing and reasoning about uncertainty. This question is vital in almost all data-driven applications. Different models of uncertainty have been proposed ranging from classical Bayesian probability theory to more general models such as those based, among others, on belief functions and possibility measures. The goal of this special session is to discuss recent methodologies for managing, reasoning, and decision-making under uncertainty from these different perspectives.