Special Sessions

At the moment the following special sessions have been confirmed:

Proposals for organizing additional special sessions can be made until Oct. 31, 2025 by contacting Martin Golumbic <golumbic@gmail.com>.

Cooperation, Competition, and Complexity in AI Planning and Learning

Organizers: Alan Kuhnle and Guni Sharon

Strategic interactions are central to many real-world AI systems, from multi-agent planning to reinforcement learning in competitive or cooperative settings. This session explores the interplay between cooperation, competition, and computational complexity in AI planning and learning. The goal is to highlight algorithmic and theoretical advances that deepen our understanding of strategic behavior in sequential decision problems. Topics will include game-theoretic learning dynamics, multi-agent reinforcement learning, submodular coordination, adversarial planning, and complexity-theoretic insights into equilibrium computation and agent interaction.

Boolean and pseudo-Boolean Functions -- Theory and Applications

Organizers: Munevver Subasi and Ersoy Subasi

Boolean and pseudo-Boolean functions play a fundamental role across a wide spectrum of disciplines, including mathematics, computer science, operations research, and various areas of engineering and science. The breadth of their applicability continues to expand, necessitating the development of new theoretical frameworks and efficient algorithmic methodologies. This track aims to provide a forum for researchers from diverse disciplines to share recent advances, showcase emerging applications, and discuss fundamental open problems that shape future directions in this field.

AI in Group Theory

Organizers: Elena Bunina and Alexei Miasnikov

This special session explores contemporary Machine Learning and AI techniques as tools for research in group theory and adjacent algebraic/combinatorial domains. We invite talks on AI-assisted conjecture generation and proof search, learning algebraic invariants and representations from data, symbolic–neural pipelines for word/conjugacy/isomorphism problems, and reinforcement or generative methods for algorithm discovery. We also welcome contributions on verification and evaluation (formal proofs, benchmarks, reproducible pipelines). The aim is to bring together mathematicians and AI researchers, showcase concrete case studies (e.g., automorphisms, Burau representations, growth and random groups), and outline open problems and datasets to accelerate progress.