Ania AI Storying Tech Paper

Ania AI Storying: A Browser-Based Rich-media Narrative Experience with Decision Analysis


Authors: Chris Harvey, Ania Temasek

 

Abstract

Ania AI Storying is a browser-based narrative game that integrates rich-media storytelling with interactive decision-making. Stories are divided into scenes, each presented through a song (hosted on streaming platforms, and used as video soundtracks) and a video with narration (hosted on platforms such as YouTube). Players predict the protagonist's next action via multiple-choice questions on the ania.ai website, using contextual clues, a character profile, and decision feedback. This white paper outlines the logic, design, and technical framework, emphasizing external asset hosting and browser-based logic.

 

1. Introduction

Ania AI Storying blends passive rich-media consumption with active decision analysis, leveraging external platforms for content delivery and ania.ai for game logic. It:

  • Delivers songs via streaming platforms and videos with narration via hosting platforms.

  • Hosts game logic, profiles, and choices on ania.ai.

  • Engages players in predicting protagonist actions using clues and feedback. The game targets fans of narrative-driven experiences, accessible via modern browsers.

 

2. Core Concept

Ania AI Storying divides a story into scenes, each comprising:

  • Rich-media Presentation:

    • Song: Streamed from platforms (e.g., Spotify, SoundCloud), embedded as the video soundtrack.

    • Video with Narration: 2D animated, hosted on YouTube, with voiceover narration, embedded on ania.ai.

  • Decision Point: A multiple-choice question (“What does the protagonist do next?”) with a number of options, hosted on ania.ai.

  • Analysis Mechanics:

    • Contextual Clues: Embedded in song lyrics, video visuals, and narration.

    • Character Profile: A dynamic record of traits and decisions on ania.ai.

    • Decision Feedback: Post-choice analysis on ania.ai showing alignment and outcomes.

Objective: Players analyse the protagonist's decisions to predict actions, earning points or unlocking story branches, with seamless integration of external assets and ania.ai logic.

 

3. Game Mechanics

 

3.1 Rich-media Clues (Option 1)

  • Purpose: Provide hints about the protagonist's motivations via song, video, and narration.

  • Implementation:

    • Song: Streamed via Spotify/SoundCloud API, embedded in YouTube video as soundtrack. Lyrics encode cues (e.g., “fearful steps” for caution). Tense tone suggests danger.

    • Video with Narration: YouTube-hosted MP4, embedded on ania.ai. Video animation shows body language (e.g., trembling hands) and environment (e.g., glowing eyes). Narration voiceover reveals thoughts (e.g., “mentor's warning”).

    • Pre-Choice Prompt: On ania.ai, JavaScript-driven question (e.g., “What emotion drives the protagonist?”) unlocks hints if correct.

  • Logic: Clues align with protagonist traits, balancing clarity and ambiguity to encourage analysis.

 

3.2 Character Profile or Journal (Option 2)

  • Purpose: Track protagonist traits and decisions to aid prediction.

  • Implementation:

    • Profile Structure: JSON object on ania.ai with attributes (e.g., “Caution: High”) and journal entries (e.g., “I can't trust the forest”).

    • Dynamic Updates: JavaScript updates profile after scenes (e.g., “Chose to hide, Caution +10”).

    • Player Interaction: HTML/CSS interface on ania.ai lets players select a trait before choosing (e.g., “Caution”).

  • Logic: The profile scaffolds reasoning, tracking the protagonist's arc to guide choices.

 

3.3 Decision Breakdown and Feedback (Option 3)

  • Purpose: Validate choices with feedback, deepening decision understanding.

  • Implementation:

    • Immediate Feedback: HTML text on ania.ai explains alignment (e.g., “Hiding fits Lila's caution, seen in her trembling hand”).

    • Consistency Score: JavaScript calculates score (e.g., 90% for canonical choices).

    • Narrative Consequence: Text or brief YouTube clip shows outcome (e.g., “Creature retreats”).

    • Analysis Mode: Replay YouTube video/song on ania.ai with CSS annotations (e.g., text overlays) and a reflection question (“Why this choice?”).

    • Community Stats: Fetch player choices via ania.ai backend API (e.g., “65% chose 'Hide'”).

  • Logic: Feedback connects choices to clues, encouraging refined analysis. Analysis Mode highlights missed hints.

 

3.4 Integrated Flow

  1. Scene Presentation: On ania.ai, stream song and embed YouTube video with narration.

  2. Analysis Prompt: On ania.ai, “What emotion drives the protagonist?” (Unlocks hint).

  3. Profile Review: Select trait on ania.ai.

  4. Choice: Pick from 4 options on ania.ai.

  5. Feedback: Show alignment, score, outcome on ania.ai.

  6. Analysis Mode: Replay video/song with annotations, answer reflection question.

  7. Profile Update: Save choice on ania.ai.

  8. Progress: Load next scene on ania.ai.

 

4. Technical Architecture

 

4.1 Platform

  • Target: Web browser (Chrome, Firefox, Safari, Edge).

  • Framework: JavaScript, HTML5 for text/imagery, Custom UI.

  • Backend: Node.js with MongoDB for player data (profiles, choices), analytics, and community stats.

 

4.2 Rich-media Delivery

  • Song: Streamed, incorporated in YouTube video.

  • Video with Narration: YouTube-hosted MP4s, embedded on ania.ai. Narration as voiceover, synced with visuals.

  • Storage: YouTube for videos, Sudo for songs, MongoDB Storage for metadata or fallback assets.

 

4.3 Decision System

  • Data Structure:

    • Scene Object: {ID, Song_URL, Video_URL, Clues[], Choices[]}.

    • Choice Object: {ID, Text, Trait_Alignment, Outcome_Text, Consequence_ID}.

    • Profile Object: {Traits{Cautious, Curious}, Journal[], Past_Choices[]}.

  • Logic Flow:

    • Load scene → Stream multimedia → Prompt analysis → Update profile → Present choices → Process choice → Deliver feedback → Save state.

  • Branching: Limited branching per scene, converging at key scenes. JavaScript state machine tracks paths.

 

4.4 Analysis Mechanics

  • Clue Integration: Tag clues in assets (e.g., lyrics with “fear,” video frames with “trembling hand”). JavaScript parser matches clues to choices.

  • Profile Updates: Rule-based (e.g., “If choice = Hide, Caution +10”). Stored in Firebase Realtime Database.

  • Feedback Algorithm:

    • Consistency score: Sum(Trait_Alignment * Clue_Matches) / Max_Score.

    • Feedback text: Template-based (e.g., “You chose [Choice], aligning with [Trait] due to [Clue]”).

    • Analysis Mode: Replay YouTube video with CSS text overlays.

 

5. Design Rationale

 

5.1 Why Rich-media?

  • Immersion: Songs and videos with narration create a cinematic feel.

  • External Hosting: Reduce ania.ai's storage load, leveraging reliable platforms.

  • Clue Variety: Lyrics, visuals, and narration offer diverse hints, enriching analysis.

 

5.2 Why Decision Analysis?

  • Engagement: Predicting actions makes players active participants.

  • Critical Thinking: Analysing clues and traits fosters inference, appealing to narrative fans.

  • Replayability: Feedback and profiles encourage revisiting scenes.

 

5.3 Why Limited Branching?

  • Feasibility: Full branching is resource-heavy. Limited branches balance variety and scope.

  • Cohesion: Converging paths maintain narrative focus.

 

5.4 Why External Platforms?

  • Scalability: Streaming and hosting handle high traffic, reducing ania.ai's server load.

  • Ease of Updates: New songs/videos can be uploaded to platforms without changing ania.ai's core logic.

  • User Familiarity: Players know YouTube/Suno, easing adoption.

 

6. Challenges and Mitigations

  • Platform Dependency:

    • Challenge: YouTube/Suno outages or API changes could disrupt assets.

    • Mitigation: Cache assets locally, maintain fallback MP3/MP4s on MongoDB Storage.

  • Production Complexity:

    • Challenge: Creating songs and videos is resource-intensive.

    • Mitigation: Use AI tools (e.g., Suno for music, Runway for video) for prototyping and production.

  • Clue Ambiguity:

    • Challenge: Clues must guide without being obvious.

    • Mitigation: Test with players, adjust clue prominence (e.g., clearer visuals, louder narration).

 

7. Potential Enhancements

  • Dynamic Audio: Adjust song intensity via JavaScript based on choices.

  • Lore Collectibles: Clickable video objects (YouTube annotations or ania.ai overlays) unlock codex entries.

  • Social Features: Share choices via X, Instagram, TikTok.

  • AI Personalization: Adapt clues based on player behaviour.

 

8. Implementation Roadmap

  1. Prototype:

    • Develop 1 scene (song on Suno, video with narration on YouTube, logic on ania.ai).

    • Use AI-generated assets.

    • Implement clues, profile, feedback for 1 path.

    • Test with 10-20 players.

  2. Launch:

    • Polish assets, fix bugs.

    • Deploy via Firebase Hosting on ania.ai.

    • Market via X, Instagram, TikTok and narrative game forums.

 

9. Conclusion

Ania AI Storying redefines browser-based narrative gaming by integrating externally hosted multimedia (Sudo songs, YouTube videos with narration) with decision analysis on ania.ai. Its clues, profiles, and feedback create an engaging, replayable experience. A focused prototype can evolve into a unique game.