Abstract: We present a comprehensive task-based approach as the foundation for developing explainable and trustworthy AI systems. A task T is formally defined as a 6-tuple including Goal, Input, Constraints, Output, verification Criterion, and domain Ontology. The framework emphasizes hierarchical decomposition, where complex goals are broken into verifiable subtasks, enabling traceable, human-interpretable explanations through satisfaction proofs (πi) for each criterion Ki. It integrates symbolic reasoning and probabilistic learning via functional systems, hierarchies, and semantic probabilistic inference for the knowledge induction. The approach supports hybrid multi-agent systems,combining LLMs for goal-oriented reasoning with logic-based agents for constraint enforcement. The key design principles ensure task-centric rchitecture, explicit criteria, ontological alignment, and criterion-centric explanations. Validation across diverse domains demonstrates its capacity to deliver mathematically verifiable, robust, and auditable AI solutions.

Cite: Goncharov S. , Vityaev E. , Sviridenko D. , Nechesov A.
Is Task-Based AI the Missing Link Between Human and Machine Reasoning?
Journal of Mathematical Sciences (United States). 2025. V.295. N1. P.4-14.

Dates:

Submitted:	Aug 9, 2025
Accepted:	Oct 12, 2025
Published print:	Nov 30, 2025

Identifiers: No identifiers

Citing: Пока нет цитирований