Research Objectives¶
Primary Research Goals¶
The Path (AI-Pi) investigates several key areas in edge AI gaming:
1. Edge AI Performance¶
- Complex AI behavior within 100ms response time
- Memory optimization under 512MB limit
- Hardware acceleration via AI HAT+
- Power consumption patterns
2. Adaptive Gaming AI¶
- Transformer-based behavior models
- Dynamic difficulty (0.2-0.95 range)
- Personality-driven actions
- Real-time adaptation
3. Hardware Optimization¶
- AI HAT+ acceleration techniques
- Memory management strategies
- Power efficiency patterns
- Thermal considerations
Research Questions¶
1. Performance Metrics¶
- Can we maintain <100ms AI response time?
- How to optimize for 512MB memory limit?
- What's the impact of hardware acceleration?
- How to balance complexity vs. speed?
2. Player Experience¶
- How do players respond to difficulty range?
- What makes AI personalities feel natural?
- How does local processing affect engagement?
- What's the optimal adaptation rate?
3. Technical Feasibility¶
- Can Transformer models run efficiently on edge?
- What are the practical model size limits?
- How to optimize hardware acceleration?
- What are the thermal constraints?
Methodology¶
1. Data Collection¶
- Game state recordings (MongoDB)
- Player action patterns
- System performance metrics
- AI decision processes
2. Analysis Approaches¶
- Response time analysis (<100ms target)
- Memory usage patterns (<512MB)
- CPU utilization (<80%)
- Temperature monitoring
3. Validation Methods¶
- Automated performance tests
- Player feedback analysis
- Hardware stress testing
- Long-term stability monitoring
Expected Outcomes¶
1. Technical Insights¶
- Edge AI performance boundaries
- Optimization techniques
- Hardware utilization patterns
- Scaling limitations
2. Player Behavior¶
- Difficulty preference patterns
- Engagement metrics
- Learning adaptation rates
- Personality preferences
3. System Design¶
- Architecture recommendations
- Hardware configuration guides
- Performance optimization patterns
- Development guidelines
Research Impact¶
1. Edge AI Gaming¶
- Feasibility demonstration
- Performance benchmarks
- Design patterns
- Implementation guides
2. Hardware Optimization¶
- AI HAT+ usage patterns
- Memory management techniques
- Power optimization strategies
- Thermal solutions
3. Development Practices¶
- Edge AI design patterns
- Testing methodologies
- Documentation standards
- Performance monitoring