Automated Planning

2 items

RESEARCHarXiv CS.AI·4/14/2026

Explainable Planning for Hybrid Systems

This thesis presents a comprehensive study on explainable artificial intelligence planning (XAIP) for hybrid systems, addressing the growing need to generate explanations for AI-based systems. It focuses on how automated planning is applied to complex and safety-critical domains like smart energy grids and self-driving cars.

Autonomous systems Hybrid Systems XAI Planning Explainable AI

RESEARCHarXiv CS.AI·4/13/2026

RAMP: Hybrid DRL for Online Learning of Numeric Action Models

RAMP proposes a novel strategy for learning numeric planning action models online through environmental interactions, integrating Deep Reinforcement Learning (DRL), action model learning, and planning. This creates a positive feedback loop where the RL policy gathers data to refine the action model, while the planner generates plans to continue training the RL policy.

Deep Reinforcement Learning Action Model Learning Numeric Planning reinforcement learning