## 4 Methodology
### Architectural Framework for Real-Time UX Adaptation  
The system architecture is illustrated in Figure 1 and was designed to process real-time interactions while ensuring modularity, UI generation, and contextual adaptation. The real-time multi-agent reinforcement learning was the core approach for enabling dynamic UI refinements, enhancing user engagement, and delivering customized UI experiences. Figure 1 also presents a four-layer architecture, including: Front-End, Back-End, and Knowledge Base MDD tool. Front-End layer communicates with the Back-End layer via Web-Sockets and RESTful APIs to enable seamless real-time interactions and dynamic UI adjustments. 1. **Front-End Layer:** This layer is responsible for displaying adaptive UI layouts, collecting user interactions and rendering optimized UI components. This layer consists of two main modules: - User Interaction Tracker Module: captures scrolls, clicks, hovers, dwell time, and gestures. It sends real-time events to the Multi-Agent System (MAS) layer for processing. - Real-time UI Adjustment and Rendering Module: This module dynamically updates and renders UI elements based on AI-driven recommendations. It consists of: - UI Component Manager: Handles UI components and maintains UI states. - UI Deployer: Converts JSON-based UI models into deployable interfaces for platforms, such as React.js, Flutter, and WebVR, using MDD tools. - UI Render: Displays the final UI layout after adaptation and optimization. Figure 1: The general architecture of AI-MDD-UX. (1) Magenta color for CIM, (2) Blue color for PIM, (3) Red color for PSM, and (4) Gray color for Code. 2. **Back-End Layer:** This layer processes user interactions, clusters user behavior, and adapts UI layouts through three main modules: - AI-Behavior Analyzer Module: This module receives user interaction logs and applies unsupervised clustering (KMeans++) to group users into different personas. It also stores personas and preferences in the Knowledge Base (KB) and shares insights with the AI-Driven UI Adaptation and Optimization Module. - AI-Driven UI Adaptation and Optimization Module: This module enables AI driven personalization and optimization. It consists of: * GAN-Based UI Generator: Generates adaptive UI layouts based on user preferences and behavioral clusters. It also uses a discriminator network to validate usability and coherence. * Multi-Agent Mediator: Acts as an intermediary between GAN and RL components, ensuring smooth decision-making based on adaptive rules and performance metrics. Multiple agents may share policies while executing dedicated adaptation tasks. * RL-Based Optimizer: Implements Q-learning-based UI refinement engine that optimizes layouts and applies logical adaptation rules for early, priority, and predictive guidance. - Continuous Improvement Module: This module monitors user engagement and interaction data to fine-tune models dynamically. It consists of * AI Performance Monitor: Monitors new user interactions and system performance while computing CTR (Click-Through Rate), engagement scores, and load time performance. * Fine-tuning Module: generate UI updates and adjust learning parameters based on user interaction metrics, and system performance. 3. **Knowledge Base Layer:** Serves as the central repository for storing historical user interactions, UI performance data, and contextual information. It includes * User Profiles: stores individual user behaviors and preferences. * Historical UI Performance: Logs past UI adaptations and their success rates, including state-action-reward experiences to improve RL learning. * Adaptation Rules: stores logical rules for priority and predictive guidance. * Persona Repository maintains essential information on user personas and preferences for personalized UI adaptation. 4.