International Conference on Logics in Artificial Intelligence, Reasoning and Compilation
(ICLAIRC-27)

This track focuses on the application of abductive and inductive reasoning techniques in artificial intelligence. It aims to explore innovative methods and frameworks that enhance reasoning capabilities in AI systems.

This session will delve into answer set programming as a powerful tool for knowledge representation and reasoning. Participants are encouraged to present novel applications and theoretical advancements in this area.

This track examines the role of argumentation systems in artificial intelligence, emphasizing their use in decision-making and conflict resolution. Contributions that showcase practical implementations and theoretical insights are welcome.

Focusing on automated reasoning, this session will cover advancements in satisfiability checking and its extensions. Researchers are invited to discuss new algorithms, tools, and applications in this rapidly evolving field.

This track will explore the computational complexity and expressiveness of various logical systems used in AI. Papers addressing theoretical foundations and practical implications are encouraged.

This session will investigate the integration of deontic logic in AI, particularly in modeling normative systems. Contributions that address ethical considerations and compliance in AI applications are particularly relevant.

This track focuses on description logics and their applications in the semantic web and ontology development. Participants are invited to share insights on enhancing data interoperability and knowledge sharing.

This session will explore methodologies for logic-based data access and integration in AI systems. Papers that propose innovative solutions for data interoperability challenges are highly encouraged.

This track addresses the use of logics designed for uncertain and probabilistic reasoning in AI applications. Contributions that bridge theoretical advancements with practical applications are sought.

This session will explore the intersection of logic and machine learning, focusing on how logical frameworks can enhance learning algorithms. Researchers are invited to present novel approaches and case studies.

This track will investigate the application of non-classical logics, such as modal and temporal logics, in multi-agent systems. Contributions that highlight their role in improving agent communication and coordination are welcome.