In artificial intelligence (AI), planning refers to the capability of an agent or system to devise a sequence of actions to achieve a given goal, starting from an initial state and adhering to various constraints. This discipline aims to automate strategic decision-making, considering the environment, available resources, uncertainties, and potential agent interactions. Planning distinguishes itself from optimization by focusing on the temporal structuring of actions and managing the complexity of sequential or parallel tasks.
Use cases and examples
Planning is fundamental in autonomous robotics (navigation, object manipulation), logistics (warehouse management, vehicle routing), task scheduling in industrial production, space mission management, and strategy games. For instance, in an automated warehouse, a planning system determines the optimal order for collecting goods. In robotics, it enables a robot to plan its movements while avoiding obstacles.
Main software tools, libraries, frameworks
Notable tools include PDDL (Planning Domain Definition Language), a standard for modeling planning problems. Widely used solvers include Fast Downward, OPTIC, and LPG. In robotics, the MoveIt! library (for ROS) offers advanced motion planning capabilities. Other frameworks like AIPlan4EU and pyperplan facilitate the integration of planning into broader systems.
Latest developments, evolutions, and trends
Planning is increasingly integrated with machine learning, allowing systems to adapt strategies based on experience. Hybrid approaches that combine symbolic planning with neural network models are on the rise, as are multi-agent and collaborative planning techniques. Current challenges include real-time planning, uncertainty management, and extension to partially observable environments. Interoperability with other AI modules (perception, dialogue) is also a dynamic research area.
