Planning is a key aspect of artificial intelligence that involves strategically selecting and organizing actions to achieve a specific goal. AI systems utilize various types of planning techniques and approaches to solve complex problems in real-world domains. The choice of technique depends on the nature of the problem, search space, computational resources, and performance objectives. This article explores some of the most common types of planning methods used in AI.
Hierarchical Planning
Hierarchical planning is a technique that breaks down a complex planning task into a hierarchy of simpler subtasks. The overall task is represented as the high-level goal, which is recursively divided into smaller subgoals or actions in multiple abstraction levels.
This approach provides several benefits:
- It reduces large problems into manageable components which are easier to solve individually.
- Planning can occur at different levels, enabling higher-level reasoning and coordination.
- Subplans can be reused instead of computing from scratch.
- It enables handling intricate tasks by abstraction and decomposition.
Hierarchical planning is commonly used in robotics, manufacturing, logistics, and other domains involving multi-step processes.
Optimal Planning
Optimal planning aims to find the most ideal or best possible plan that achieves the goal optimally as per specified performance metrics. It utilizes optimization algorithms like linear programming, dynamic programming, and graph search techniques to identify the optimal plan.
Some optimal planning methods include:
- A* search which uses heuristics to guide optimal pathfinding.
- Dynamic programming that solves subproblems optimally in a bottom-up manner.
- Evolutionary algorithms that iteratively improve solutions based on bio-inspired operators.
Optimal planners are useful for applications like scheduling, path planning, and resource allocation where plan quality is critical.
Partial Order Planning
Partial order planning maintains flexibility by only ordering actions when dependencies force it. Unlike total-order planning which sequences all actions upfront, partial order planners start with a partial plan and incrementally add ordering constraints between actions as needed.
Benefits of partial order planning:
- Retains flexibility for as long as possible.
- Avoids premature commitments to a specific ordering.
- Easier to modify plans on-the-fly if new constraints emerge.
- Useful in dynamic environments with uncertainty.
Partial order planning is often used in domains like logistic planning, molecular biology, and flexible manufacturing.
Informed Search Methods
Informed search leverages domain knowledge in the form of heuristics to guide the search process towards promising solutions. Unlike uninformed search which explores blindly, informed search ranks options intelligently using estimated goal distance, cost, or utility.
Some informed search techniques used in planning:
- A* combines cost-to-reach and estimated cost-to-goal to focus search.
- Greedy search selects locally optimal choices to construct a solution.
- Beam search maintains multiple candidate solutions simultaneously.
Informed search enables efficient planning in large search spaces and is used in pathfinding, scheduling, and optimization.
Overall, AI planning techniques aim to balance optimality, flexibility, and computational efficiency based on the problem context. As planning research advances, newer hybrid techniques are emerging to combine the strengths of different approaches for robust performance.
Conclusion
Planning is an integral capability for building intelligent systems that can reason, solve problems autonomously, and achieve complex objectives. Hierarchical, optimal, partial order, and informed search are some of the popular techniques used in AI planning, each with its own strengths and tradeoffs. Understanding these methods allows selecting an appropriate approach based on the planning problem, constraints, and performance goals. Advances in planning will enable AI systems to tackle more ambiguous, intricate real-world problems.
References
- Russell, Stuart J., and Peter Norvig. “Artificial intelligence: a modern approach.” (2021).
- Ghallab, Malik, Dana Nau, and Paolo Traverso. “Automated Planning and Acting.” (2016).
- Likhachev, Maxim, et al. “Anytime dynamic A*: An anytime, replanning algorithm.” Proceedings of the International Conference on Automated Planning and Scheduling. 2008.