Artificial Intelligence

Multi-Agent Daily Planning System (MADPS): A GAIA-Based Architecture for Dynamic Scheduling

This document outlines the GAIA methodology design for a single-user Multi-Agent Daily Planning System (MADPS) engineered to generate, adapt, and personalize daily schedules. The architecture consists of three core autonomous components: the User Profile Agent (UPA), the Task Management Agent (TMA), and the Planning & Adaptation Agent (PAA). MADPS is designed to maximize priority satisfaction under constraints, minimize re-planning latency and schedule churn during task disruptions, and personalize schedules by learning user energy curves and preference weights. To support these objectives, the system employs FIPA-style interaction protocols and a dual-strategy PAA workflow, utilizing rapid, greedy local repairs for minor schedule disruptions and Genetic Algorithm (GA)-driven global optimization for system-wide impacts. Proposed schedules are evaluated using a comprehensive fitness function that carefully weights priority satisfaction, energy alignment, transition minimization, and hard constraint penalties to ensure feasible and highly optimized daily plans.