Planningsearch

Planningsearch is a term used in artificial intelligence to describe an integrated approach that combines automated planning with search-based problem solving. It views the planning process as the construction of a sequence of actions to achieve a goal, but relies on search techniques to explore the space of possible states and actions in order to find feasible, robust, or optimal plans. In planningsearch, planners often apply heuristic search (such as A*, hill-climbing with admissible heuristics, or domain-specific search strategies) to navigate either the state space or the plan space, allowing for dynamic adaptation when plans fail or the environment changes.

There are several variants. State-space planning emphasizes searching through states reachable by actions to reach the goal, while plan-space or partial-order planning focuses on manipulating the structure of plans themselves and then validating feasibility. Hybrid approaches interleave planning and search steps, enabling replanning in response to unexpected events or uncertain models. The choice of representation (for example, PDDL-like domain models) and the search strategy strongly influences performance and scalability.

Applications of planningsearch appear in robotics, autonomous systems, logistics and scheduling, and disaster response, where environments are complex and subject to change. The approach supports robustness by enabling on-the-fly re-planning and by balancing plan optimality against computation time. See also: automated planning, heuristic search, HTN planning, reinforcement learning, planning under uncertainty, search algorithms.