Constraint Inference

Constraint inference aims to automatically discover hidden limitations or rules governing a system's behavior, typically from observational data like expert demonstrations. Current research focuses on developing efficient algorithms, often employing Bayesian methods or inverse optimization techniques, to infer these constraints, particularly within the context of reinforcement learning and robotics. This field is crucial for safely deploying autonomous agents in complex environments where explicitly defining all constraints is impractical, improving the reliability and safety of AI systems in real-world applications. Recent work emphasizes benchmarking and developing more robust methods that handle noisy or incomplete data and varying constraint severities.