Feasible Action

Feasible action research focuses on identifying and generating action sequences that achieve desired outcomes within computational and environmental constraints. Current work explores this across diverse domains, employing methods like Bayesian optimization for efficient search in constrained spaces, neuro-symbolic models integrating learned representations with symbolic planning for robot control, and reinforcement learning techniques to generate optimal policies from limited data, often incorporating explainability for improved transparency. This research is crucial for advancing artificial intelligence, particularly in robotics and human-robot collaboration, by enabling more robust and adaptable systems capable of operating effectively in complex, real-world scenarios.