1. Operational Focus
Designed a modular research harness architecture for empirical evaluation of calibrated autonomy in smart-home AI. The work shifted the research from conceptual autonomy levels toward a reproducible experimental framework capable of evaluating multiple decision models under identical environmental conditions.
2. System Environment & Bounds
- Target Architecture / Platform: Modular Python research harness
- Operating Environment: Simulation-based smart-home digital twin
- Test Scenario / Configuration: Pluggable autonomy controllers (rule-based, heuristic, and AI agent) evaluated using common scenarios, governance policies, telemetry collection, and statistical analysis.
- Primary Components: Configuration, environment simulation, autonomy modules, governance layer, observability layer, analysis pipeline, experiment runner.
3. Execution Log
- Phase 1: Designed a flexible project architecture separating experiment orchestration, environment simulation, autonomy implementations, governance, observability, and statistical analysis.
- Phase 2: Standardized controller abstraction through a common autonomy interface to allow interchangeable decision engines.
- Phase 3: Defined simulation concepts including world state, action representation, intervention tracking, telemetry generation, and scenario-driven execution.
- Phase 4: Planned configuration-driven experimentation using reusable YAML scenario definitions and centralized experiment parameters.
- Phase 5: Structured automated post-processing through metrics aggregation, statistical analysis, replay capability, and report generation.
4. Telemetry, Anomalies & Outcomes
- Resolution / Next Steps: Implement the core experiment runner, digital world simulator, telemetry pipeline, and baseline rule-based controller before integrating heuristic and AI-driven autonomy models.