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International Conference on Logics in Artificial Intelligence and Machine Learning

📅 4–5 Jun 2027 📍 Novosibirsk, Russia 👤 Standard / Listener

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$185

virtual · $185 in person

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Conference session tracks

Key research areas covered across the sessions — tap a track to read more.

This track focuses on the application of abductive and inductive reasoning techniques within artificial intelligence systems. Participants will explore methodologies that enhance machine learning models through logical inference.

This session will delve into the principles and applications of answer set programming in solving complex problems. Researchers will present innovative uses of this declarative programming paradigm in AI.

This track examines the role of argumentation systems in artificial intelligence, emphasizing their significance in decision-making processes. Contributions will highlight the integration of logical frameworks in argumentation theory.

This session addresses advancements in automated reasoning techniques, including satisfiability checking and its extensions. Participants will discuss the implications of these techniques for AI applications.

This track investigates the computational complexity associated with various logical systems and their expressiveness. Researchers will present findings that bridge theoretical insights with practical applications in AI.

This session focuses on deontic logic and its role in modeling normative systems within artificial intelligence. Discussions will include the implications of normative reasoning in ethical AI systems.

This track explores the intersection of description logics and the semantic web, emphasizing their contributions to knowledge representation. Participants will share insights on enhancing ontological frameworks through logical approaches.

This session is dedicated to the latest advancements in knowledge representation and reasoning techniques in AI. Contributions will highlight innovative methods for compiling and accessing knowledge bases.

This track examines the synergy between logic programming and constraint programming in solving computational problems. Researchers will discuss frameworks that leverage both paradigms for enhanced problem-solving.

This session focuses on the application of logics designed for uncertain and probabilistic reasoning in AI. Participants will present methodologies that address challenges in reasoning under uncertainty.

This track investigates the application of various logics in multi-agent systems, games, and social choice theory. Discussions will center on how logical frameworks can facilitate cooperation and decision-making among agents.