uGAME
uGAME integrates vision AI, memory analysis, and a knowledge engine to create AI game agents that understand game mechanics at a detailed level. The system processes both visual and memory data to mak
Core Intelligence System
┌─────────────────────────────────────────────────────────────────┐
│ NEURAL GAMING CORTEX │
└─────────────────────────────────────────────────────────────────┘
▲ ▲
│ │
▼ ▼
┌───────────────────────┐ ┌───────────────────────────┐
│ │ │ │
│ VISION CORTEX │◄────►│ MEMORY CORTEX │
│ │ │ │
└─────────┬─────────────┘ └─────────────┬─────────────┘
│ │
▼ ▼
┌───────────────────────┐ ┌───────────────────────────┐
│ │ │ │
│ SCREEN ANALYSIS │ │ RAM INTROSPECTION │
│ • Deep CV Models │ │ • Memory Mapping │
│ • Pixel Recognition │ │ • State Tracking │
│ • Text Extraction │ │ • Data Structure │
│ │ │ Analysis │
└─────────┬─────────────┘ └─────────────┬─────────────┘
│ │
└────────────────┬─────────────────┘
▼
┌─────────────────────────────────────────────────────────────────┐
│ STRATEGIC DECISION ENGINE │
└───────────────────────────────┬─────────────────────────────────┘
▼
┌─────────────────────────────────────────────────────────────────┐
│ GAME CONTROL INTERFACE │
└─────────────────────────────────────────────────────────────────┘Key Technical Components
1. Game Analysis System
The computer vision system processes game screens to understand context:
Real-time scene analysis for game state recognition
Response optimization for time-sensitive decisions
Pattern recognition for game elements and situations
Frame sequence analysis for temporal context understanding
Brightness and contrast normalization for consistent recognition
2. Memory Introspection
Memory mapping enables direct access to game state data:
Memory address mapping of relevant gameplay variables
Concurrent monitoring of multiple memory regions
State prediction based on game engine understanding
Error handling for emulation inconsistencies
High-frequency memory polling for state changes
3. Knowledge Engine
The knowledge system stores and processes game-specific information:
Comprehensive game data organization
Hybrid rule-based and ML approach to decision making
Runtime knowledge acquisition and storage
Optimized data structures for rapid information retrieval
Relational data modeling for game concept connections
AI Decision Process Visualization
The system can expose its decision-making process for debugging:
┌───────────────────── AI DECISION PROCESS ─────────────────────────┐
│ │
│ PERCEPTION: │
│ • Visual: Game screen elements detected │
│ • Memory: Current game state variables │
│ • Context: Situation analysis │
│ │
│ ANALYSIS: │
│ • Game elements and properties │
│ • Available actions │
│ • Strategic options │
│ • Outcome probability assessment │
│ │
│ STRATEGY: │
│ 1. Action prioritization │
│ 2. Alternative approaches │
│ 3. Goal alignment │
│ │
│ ACTION: Selected game input │
└──────────────────────────────────────────────────────────────────┘Control System
The control interface converts decisions to precise game inputs:
Timing calibration for consistent button press duration
Multi-button sequence execution
Command queuing for complex input sequences
Input verification through state monitoring
Capabilities
uGAME agents can demonstrate:
Retention of game map layouts, item locations, and dialogue options
Strategy optimization based on game mechanics
Efficient pathfinding and route planning
Error recovery in navigation and interaction
Performance improvement through reinforcement learning
Technical Architecture
Core Components
The system consists of five primary subsystems:
Game Memory Reader - Accesses game memory data structures
Visual Understanding System - Captures and analyzes game screens
Game Knowledge Database - Stores game rules and environment data
Strategic Decision Maker - Plans actions based on current state
Task Model System - Manages hierarchical goal execution
System Integration
┌───────────────────────────────────────────────────────────────┐
│ User Interface │
│ ┌─────────────────┐ ┌───────────────────┐ │
│ │ Game │◄──────────────────┤ AI Assistant │ │
│ │ Interface │ │ Interface │ │
│ └────────┬────────┘ └─────────┬─────────┘ │
└───────────┼────────────────────────────────────┼──────────────┘
│ │
▼ ▼
┌───────────────────────┐ ┌───────────────────────────┐
│ Button Control │◄───────────►│ Master Intelligence │
│ System │ │ Loop │
└───────────┬───────────┘ └───────────┬───────────────┘
▲ │
│ ▼
│ ┌───────────────────────────┐
│ │ Task Model System │
│ │ • Templates │
│ │ • Subtasks │
│ │ • Progress Tracking │
│ └───────────┬───────────────┘
│ │
│ ▼
┌───────────────────────┐ ┌───────────────────────────┐
│ Navigation & │◄────────────┤ Game Data Analyzer │
│ World Interaction │ └───────────┬───────────────┘
└───────────────────────┘ │
▼
┌───────────────────────────┐
│ Vision & Memory Access │
└───────────┬───────────────┘
▲
│
┌───────────────────────────┐
│ Game Knowledge Base │
└───────────────────────────┘ Task Model Implementation
The Task Model System implements a structured approach to gameplay:
interface TaskContext {
taskType: string; // Type of task (NAVIGATE, BATTLE, etc.)
targetName?: string; // Specific target or location
progress: TaskProgress; // Completion tracking
variables: Record<string, any>; // Task state
}
// Subtask definition
interface Subtask {
id: string;
type: SubtaskType; // NAVIGATE, BATTLE, USE_MENU, etc.
requiredScreenType: string[]; // When this subtask applies
completionCriteria: (gameState, context) => boolean;
action: (gameState, context) => AgentAction;
priority: number;
dependsOn?: string[]; // Prerequisites
}Input Processing
The system supports natural language commands for agent control:
Exploration: "Explore the area" or "Look for items"
Strategic Actions: "Find and capture target" or "Complete objective X"
Navigation: "Go to location Y" or "Find character Z"
Direct Control: "Press button sequence" or "Execute combo"
Task Control: "Stop current task" or "What are you doing now?"
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