Special Sessions

ISAIM 2018 will feature several special sessions:

Flow Optimization in Traffic Networks

Organizers: Michael Albert and Guni Sharon

Connected and autonomous vehicle technology has advanced rapidly in recent years. These technologies create possibilities for advanced AI-based traffic management techniques. Developing such techniques is an important challenge and opportunity for the AI community as it requires synergy between experts in game theory, multiagent systems, behavioral science, and flow optimization. This special session aims to bring together scientists across these disciplines to discuss the latest results and research challenges.

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.

Formalising Robot Ethics

Organizers: Michael Fisher and Marija Slavkovik

In the future autonomous robotic systems are expected to be common, not only in factories and on our roads, but in domestic and health-care situations. This new generation of intelligent machines will be required to act autonomously, yet function as part of our society. Societally integrated machines will encounter not just safety issues, but ethical issues. While there has been a large amount of work in Philosophy on a range of ethical theories, building verifiable/certifiable ethical behaviour into artificial agents still remains a problem. A key aspect of this is to be able to, precisely and unambiguously, formalise exactly what ethical behaviour is required. This session is intended to bring together researchers working on robot/machine ethics, particularly the formalisation of these, and to discuss the latest results, the open problems, and the main challenges for the future.

Topological Reasoning and Data Analyses

Organizer: Debasis Mitra

Qualitative spatial and temporal reasoning within Artificial Intelligence and Constraint Programming has many rich and unique theoretical results. The problem formulation and approaches there are often implicitly built upon topological relationship structures. Topology, as a branch of mathematics, studies representation and measurement of shapes. Computational topologists have been building up a sophisticated paradigm for applying algebraic topology in understanding shapes in large scale data.

In this special session we are seeking research questions, problem formulations and results where these two apparently disparate areas converge. We are also seeking to understand how these two areas may mutually enrich each other.

Example research questions that one may ask are:
Does qualitative spatio-temporal representation has a formal topological underpinnings?
Can complexity results in QSTR allow TDA to become more efficient?
Can QSTR problem formulation be modified toward machine learning?
Can images be analyzed based on their inherent spatial topological invariants?

Theory of Machine Learning

Organizer: Lev Reyzin

Machine learning theory focuses on deriving algorithms with provable guarantees and on developing models that capture various learning phenomena. Its advances have lead to new insights and practical approaches to a variety of machine learning and AI challenges. Machine learning theory, as a field, draws on a variety of tools from theoretical computer science, mathematics, optimization, and statistics. This special session aims to bring together scientists across these disciplines to discuss the latest results and research challenges.