## 4 Methodology
### The Workflow for Real-Time UI/UX Adaptation in AI-MDD-UX
#### 4.3.5 Multi-Agents Reinforcement Learning Based UI Optimization
Three distinct guidance strategies are used based on interaction patterns: predictive adjustments for routine optimizations, priority-based adaptations for urgent situations and proactive refinements anticipating user needs. (1) **Early Guidance Strategy (Algorithm 3)**  
When multiple actions are equally important, the system prioritizes agent actions using Early Guidance Strategy. The _User Behavior Agent_ tracks user interactions and guides the _UI Layout Agent_ to apply immediate changes. However, the system implements mobile-first adaptations when detecting smaller screens, automatically enlarging form fields and highlighting interactive elements. For desktop interfaces, it enhances visual feedback through dynamic color changes, creating a responsive experience tailored to each device's interaction paradigms. ```
Inputs: \(s\): UI State, UAgt: Urgency Agent, CRA: Conflict Resolution Agent Outputs: UIL: UI Layout. Begin (1) : Foreach UI state (s) monitored by UBA Do (2) : If device type = mobile Then (3) : If form interaction detected Then (4) : Guide (\(\pi\)(s), UI Layout Agent) //UBA suggests increasing form  field size. (5) : UIL \(\leftarrow\) Enlarge Form Fields (6) : If button hover detected Then  Guide (\(\pi\)(s), UI Layout Agent) // UBA advises UI Agent to  highlight buttons. (8) : UIL \(\leftarrow\) Highlight Button (9) : Else if the device type = Desktop Then (10) : If button click detected Then (11) : Guide (\(\pi\)(s), UI Layout Agent) // UBA recommends changing button color (12) : UIL \(\leftarrow\) Change Button Color (13) : End For (14) : Return UIL  End
```  
**Algorithm 3** Early Guidance for UI Layout Adaptation2. **Priority Guidance Strategy (Algorithm 4)** When critical notifications emerge, the system immediately suspends routine optimizations to prioritize urgent alerts through prominent visual treatments, ensuring users never miss time-sensitive information regardless of other interface modifications in progress. This strategy uses predefined urgency thresholds and context-awareness rules to determine when to trigger priority guidance. Additionally, fallback mechanisms are employed to re-engage users with suspended tasks once the alert has been acknowledged. ```
Inputs: \(s\): UI State, Augst: Urgency Agent, CRA: Conflict Resolution Agent Outputs: UIL: UI Layout. Begin (1) : Foreach UI state (s) monitored by UBA Do (2) : If urgent alert detected Then (3) : Guide (\(\pi\)(s), UI Layout Agent) //Ensures the user responds to the alert immediately. (4) : UIL \(\leftarrow\) Prioritize Alert (e.g., pop-up, emphasis) (5) : End If (6) : End For (7) : Return UIL End
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**Algorithm 4** Priority Guidance for UI Layout Adaptation  
(3) **Predictive Guidance Strategy to UI Layout Adaptation:** By analyzing engagement levels, device characteristics, and interaction history, the system anticipates user needs, preemptively adjusting layouts for mobile users or streamlining form interactions for frequent completers, often implementing improvements before explicit requests occur. For instance, if a mobile device is detected, the layout is reshaped before interaction, ensuring elements are stacked vertically for better accessibility. Based on past interactions, if the user frequently fills out forms, the UI automatically expands form fields to improve input efficiency (Algorithm 5). ```
Inputs: \(U\)EngLyL: User Engagement Level, ScrSize: Screen Size PastInt: Past Interaction History, CtxAw: external factors (e.g., location, time of day). UIL: UI application. // Manage Dynamic UI refinements Outputs: UIL adapt: UI Layout adaptation before user requests.