Towards Reasonable AI: Foundations for Abstraction and Generalized Reasoning

摘要

Human reasoning relies on abstraction and generalization, in order to make decisions flexible under changing conditions while ignoring irrelevant details and focusing on the essence. Developing AI systems with such abilities, while ensuring transparency and explainability on the reasoning behind the made decision, remains a central challenge. Symbolic AI has a strong position for explainable reasoning but existing generalization methods either suffer from not discovering the underlying patterns or are domain-dependent, due to a lack of theoretical characteristics for generalized reasoning. This paper presents an overview on the research towards filling this gap by developing formal and computational methods for abstraction in Answer Set Programming, one of the core formalisms in symbolic AI, and related logic-based frameworks. The contributions span from foundational abstraction techniques that simplify reasoning representations while preserving essential solution properties, to investigations on how such abstractions can both improve computational reasoning and support human understanding of AI decision processes.