Artificial Life TRIZ Cases

Joint neural networks learn state, reward, and value predictions together, reducing model mismatch, labeled data needs, and planning overhead.

Automated reward and network shaping helps robots learn robust navigation policies despite sparse rewards, dynamic obstacles, and new environments.

Annotated LwM2M sensor data separates controllable from uncontrollable metrics, cutting AI training time and improving IoT scalability.

Local Q-table updates and diffusion-based propagation help agents adapt to new obstacles without delaying policy refresh across the full environment.

Fusing camera-based body detection with sensor-based leg detection helps a robot keep following the intended person in cluttered spaces.

Reinforcement learning adjusts driving-source actions from sheet position and reward data to cut jams and downtime under changing conditions.

Historical terminal data trains ML models to predict handover settings that cut handover failures and improve network efficiency.

Coarse-state planning and auxiliary value loss help RL agents navigate varied terrain with higher success rates and lower performance variance.

Deep reinforcement learning sets PID parameters from measured plant states to reduce loop interference and stabilize complex process control.

Attention layers highlight salient environmental features so simulation-trained agents adapt to real-world mismatch with less retraining.

A factory simulator generates state-action-reward data to train scheduling agents faster and adapt workflow decisions to changing process conditions.

Acceleration and angular velocity inputs are clustered without supervision so a robot can adapt movement and emotional responses to user interaction.

An integrated predictron jointly learns state transitions and value prediction, improving reward estimates while adapting planning steps to save computation.

Attention layers highlight salient environmental features so autonomous agents transfer simulation-trained policies more reliably to real-world conditions.

Nearby robots share alert mode requests when a monitored target turns abnormal, balancing service to their own users with cooperative support.

Part-specific shape feedback guides machine learning to adjust wire EDM conditions where defects occur, improving accuracy and machining efficiency.

Graph neural networks model industrial process dependencies in a knowledge graph to improve anomaly detection and contextual diagnosis.

Low-cost sensor arrays gain high-precision machine control by learning from expensive reference sensors through CNN-LSTM motion prediction.

Real-object traffic simulation trains autonomous driving control algorithms with sensor wear and variability that virtual data alone cannot capture.

ML-based substrate selection uses current manufacturing data to meet operation time constraints, cut unusable wafers, and improve throughput.

Reinforcement learning corrects wire EDM corner paths from machining conditions and environment to reduce shear drop and machining time.

Grouped graph embeddings and local plus global rewards let heterogeneous agents learn separate control policies while preserving scalable system-wide adaptation.

TD error with component-wise perturbation estimates a gradient matrix for faster feedback coefficient updates in reinforcement learning control.

A diverse set of safe policies improves online reinforcement learning speed while preserving performance through KL-divergent exploration.

Unsupervised world graph discovery gives HRL agents high-level environment structure, improving sample efficiency and downstream task performance.