Personalized nutrition systems balance strict constraints (e.g., allergies) and dynamic user adaptation. While Answer Set Programming (ASP) offers robust optimization, it lacks the cognitive flexibility for real-time belief revision. We introduce a hybrid dietary framework integrating ASP optimization with L-DINF, an epistemic logic for cognitive agents. This approach translates static “Blueprint Personas” into dynamic agents capable of introspection and negotiation by reasoning about beliefs, intentions, and the feasibility of actions. Implemented in the DALI agent language, this integration achieves the adaptive, explainable decision-making critical for modern dietary coaching.
From ASP to L-DINF: Towards Epistemic Dietary Agents for Personalized Nutrition
Vozna, Alina
2026-01-01
Abstract
Personalized nutrition systems balance strict constraints (e.g., allergies) and dynamic user adaptation. While Answer Set Programming (ASP) offers robust optimization, it lacks the cognitive flexibility for real-time belief revision. We introduce a hybrid dietary framework integrating ASP optimization with L-DINF, an epistemic logic for cognitive agents. This approach translates static “Blueprint Personas” into dynamic agents capable of introspection and negotiation by reasoning about beliefs, intentions, and the feasibility of actions. Implemented in the DALI agent language, this integration achieves the adaptive, explainable decision-making critical for modern dietary coaching.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
