UPMD
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UPMD Examples

Explore practical examples of UPMD in action. These examples demonstrate how to use UPMD for various AI interaction scenarios.

Conversational AI with Context Awareness
This example demonstrates how to create a conversational AI that adapts to the user's expertise level.
upmd
|context: expertise| = {beginner: 0.3, intermediate: 0.5, expert: 0.2}

|dynamic_grammar|
[beginner] Use simple terms and provide detailed explanations
[intermediate] Use domain-specific terms with moderate explanations
[expert] Use advanced terminology with concise explanations
|/dynamic_grammar|

|function: explain_concept(@concept)|
|if: @context.expertise == "beginner"|
  Explain @concept in simple terms with analogies
|else_if: @context.expertise == "intermediate"|
  Provide a detailed explanation of @concept with some technical terms
|else|
  Give a concise, technical explanation of @concept
|/if|
|/function|

When the user asks about artificial intelligence:
|explain_concept("artificial intelligence")|
Creative Writing Assistant
This example shows how to use quantum variables to generate diverse creative content.
upmd
|quantum_variable: writing_style| = {descriptive: 0.6, dialogue_heavy: 0.4}
|quantum_variable: emotional_tone| = {uplifting: 0.7, melancholic: 0.3}

|quantum_entanglement: writing_style, emotional_tone|
[descriptive, uplifting] = 0.8
[dialogue_heavy, melancholic] = 0.9
|/quantum_entanglement|

|function: generate_scene(@setting, @characters)|
  1. Establish the setting details based on @writing_style
  2. Introduce characters with appropriate depth
  3. Create atmosphere reflecting @emotional_tone
  4. Develop scene progression with natural flow
  |return: scene_text|
|/function|

|function: generate_dialogue(@characters, @conflict)|
  1. Create natural-sounding dialogue reflecting character personalities
  2. Incorporate subtle tension related to @conflict
  3. Balance exposition and character development
  |return: dialogue_text|
|/function|

|main_process|
  @scene = |call: generate_scene("autumn forest", ["elderly wizard", "young apprentice"])|
  @dialogue = |call: generate_dialogue(["elderly wizard", "young apprentice"], "forbidden knowledge")|
  
  |output: @scene + @dialogue|
|/main_process|

|feedback_loop|
  [positive] reinforce_current_settings()
  [negative] adjust_settings({
    writing_style: shift_towards("dialogue_heavy"),
    emotional_tone: shift_towards("uplifting")
  })
|/feedback_loop|
Personalized Learning Content Generator
This example demonstrates how to create educational content that adapts to different learning styles and levels.
upmd
|context: learner_level| = {elementary: 0.2, high_school: 0.5, college: 0.3}
|context: learning_style| = {visual: 0.6, auditory: 0.2, kinesthetic: 0.2}

|dynamic_grammar|
  [elementary] Use simple vocabulary, short sentences, and many examples
  [high_school] Use moderate vocabulary, clear explanations, and relevant examples
  [college] Use technical terminology, complex concepts, and scholarly references
|/dynamic_grammar|

|function: create_lesson_plan(@topic)|
  1. Identify key concepts based on @learner_level
  2. Structure content progression
  3. Design activities matching @learning_style
  4. Create assessment methods
  |return: lesson_plan|
|/function|

|function: generate_examples(@topic, @learner_level)|
  1. Create age-appropriate examples
  2. Connect examples to real-world applications
  3. Ensure examples illustrate key concepts clearly
  |return: examples_list|
|/function|

|multimodal_output|
  [text] detailed_lesson_plan
  [visual] concept_diagrams(@topic, @learner_level)
  [interactive] practice_exercises(@topic, @learner_level)
|/multimodal_output|

|main_process|
  @lesson = |call: create_lesson_plan("photosynthesis")|
  @examples = |call: generate_examples("photosynthesis", @learner_level)|
  
  |output: @lesson + @examples|
|/main_process|

|self_optimize|
  goal: learning_effectiveness
  criteria:
    - concept_clarity
    - engagement_level
    - knowledge_retention
    - practical_application
  interval: every_5_lessons
|/self_optimize|

|feedback_loop|
  [positive] reinforce_current_settings()
  [negative] adjust_settings({
    learning_style: adapt_to_user_preference,
    content_complexity: adjust_to_comprehension_level,
    example_quantity: increase
  })
|/feedback_loop|
Comprehensive Translation System
This example shows a complete translation system that incorporates all major UPMD features.
upmd
|quantum_variable: translation_approach| = {literal: 0.3, free: 0.7}
|context: expertise| = {beginner: 0.1, intermediate: 0.3, expert: 0.6}

|dynamic_grammar|
  [beginner] Use simple terms and detailed explanations
  [intermediate] Use translation-specific terms with moderate explanations
  [expert] Use advanced literary and translation terms with concise explanations
|/dynamic_grammar|

|function: analyze_text(@text)|
  1. Identify genre
  2. Determine historical context
  3. Understand author's intent
  4. Analyze overall tone
  5. Recognize cultural references
  6. Identify linguistic features (dialect, slang, metaphors, etc.)
  |return: analysis_result|
|/function|

|function: select_translation_strategy(@analysis_result)|
  |quantum_entanglement: genre, translation_approach|
    [poetry, free] = 0.8
    [novel, literal] = 0.6
    [essay, free] = 0.7
  |/quantum_entanglement|
  
  1. Select appropriate translation approach based on text characteristics
  2. Determine expression style suitable for target language readers
  3. Prepare alternatives for overcoming cultural differences
  |return: selected_strategy|
|/function|

|function: perform_translation(@text, @selected_strategy)|
  1. Maintain balance between accuracy and fluency
  2. Preserve style and tone
  3. Adjust cultural context
  4. Consider rhythm and meter
  5. Apply creative reinterpretation
  6. Maintain consistency
  |return: translated_text|
|/function|

|multimodal_input|
  [text] original_source_text
  [voice] native_speaker_audio (optional)
  [image] related_visual_material (optional)
|/multimodal_input|

|multimodal_output|
  [text] final_translation
  [voice] target_language_reading_voice(final_translation)
  [notes] translation_process_explanation
|/multimodal_output|

|main_process|
  @analysis_result = |call: analyze_text(original_source_text)|
  @selected_strategy = |call: select_translation_strategy(@analysis_result)|
  @translated_text = |call: perform_translation(original_source_text, @selected_strategy)|
  @reviewed_text = |call: review_and_edit(@translated_text)|
  @final_translation = |call: final_check(@reviewed_text)|
  
  |output: @final_translation|
|/main_process|