planning with llms
**Planning with LLMs** involves using **large language models to generate action sequences that achieve specified goals** — leveraging LLMs' understanding of tasks, common sense, and procedural knowledge to create plans for robots, agents, and automated systems, bridging natural language goal specifications with executable action sequences.
**What Is AI Planning?**
- **Planning**: Finding a sequence of actions that transforms an initial state into a goal state.
- **Components**:
- **Initial State**: Current situation.
- **Goal**: Desired situation.
- **Actions**: Operations that change state.
- **Plan**: Sequence of actions achieving the goal.
**Why Use LLMs for Planning?**
- **Natural Language Goals**: LLMs can understand goals expressed in natural language — "make breakfast," "clean the room."
- **Common Sense**: LLMs have learned common-sense knowledge about how the world works.
- **Procedural Knowledge**: LLMs have seen many examples of plans and procedures in training data.
- **Flexibility**: LLMs can adapt plans to different contexts and constraints.
**How LLMs Generate Plans**
1. **Goal Understanding**: LLM interprets the natural language goal.
2. **Plan Generation**: LLM generates a sequence of actions.
```
Goal: "Make a cup of coffee"
LLM-generated plan:
1. Fill kettle with water
2. Boil water
3. Put coffee grounds in filter
4. Pour hot water over grounds
5. Wait for brewing to complete
6. Pour coffee into cup
```
3. **Refinement**: LLM can refine the plan based on feedback or constraints.
4. **Execution**: Actions are executed by a robot or system.
**LLM Planning Approaches**
- **Direct Generation**: LLM generates complete plan in one shot.
- Fast but may not handle complex constraints.
- **Iterative Refinement**: LLM generates plan, checks feasibility, refines.
- More robust for complex problems.
- **Hierarchical Planning**: LLM decomposes goal into subgoals, plans for each.
- Handles complex tasks by breaking them down.
- **Reactive Planning**: LLM generates next action based on current state.
- Adapts to dynamic environments.
**Example: Household Robot Planning**
```
Goal: "Set the table for dinner"
LLM-generated plan:
1. Navigate to kitchen
2. Open cabinet
3. Grasp plate
4. Place plate on table
5. Repeat steps 2-4 for additional plates
6. Grasp fork from drawer
7. Place fork next to plate
8. Repeat steps 6-7 for additional forks
9. Grasp knife from drawer
10. Place knife next to plate
11. Repeat steps 9-10 for additional knives
12. Grasp glass from cabinet
13. Place glass on table
14. Repeat steps 12-13 for additional glasses
```
**Challenges**
- **Feasibility**: LLM-generated plans may not be physically feasible.
- Example: "Pick up the table" — table may be too heavy.
- **Solution**: Verify plan with physics simulator or feasibility checker.
- **Completeness**: Plans may miss necessary steps.
- Example: Forgetting to open door before walking through.
- **Solution**: Use verification or execution feedback to identify gaps.
- **Optimality**: Plans may not be optimal — longer or more costly than necessary.
- **Solution**: Use optimization or search to improve plans.
- **Grounding**: Mapping high-level actions to low-level robot commands.
- Example: "Grasp cup" → specific motor commands.
- **Solution**: Use motion planning and control systems.
**LLM + Classical Planning**
- **Hybrid Approach**: Combine LLM with classical planners (STRIPS, PDDL).
- **LLM**: Generates high-level plan structure, handles natural language.
- **Classical Planner**: Ensures logical correctness, handles constraints.
- **Process**:
1. LLM translates natural language goal to formal specification (PDDL).
2. Classical planner finds valid plan.
3. LLM translates plan back to natural language or executable actions.
**Example: LLM Translating to PDDL**
```
Natural Language Goal: "Move all blocks from table A to table B"
LLM-generated PDDL:
(define (problem move-blocks)
(:domain blocks-world)
(:objects
block1 block2 block3 - block
tableA tableB - table)
(:init
(on block1 tableA)
(on block2 tableA)
(on block3 tableA))
(:goal
(and (on block1 tableB)
(on block2 tableB)
(on block3 tableB))))
Classical planner generates valid action sequence.
```
**Applications**
- **Robotics**: Plan robot actions for manipulation, navigation, assembly.
- **Virtual Assistants**: Plan sequences of API calls to accomplish user requests.
- **Game AI**: Plan NPC behaviors and strategies.
- **Workflow Automation**: Plan business process steps.
- **Smart Homes**: Plan device actions to achieve user goals.
**LLM Planning with Feedback**
- **Execution Monitoring**: Observe plan execution, detect failures.
- **Replanning**: If action fails, LLM generates alternative plan.
- **Learning**: LLM learns from failures to improve future plans.
**Example: Replanning**
```
Initial Plan: "Pick up cup from table"
Execution: Robot attempts to grasp cup → fails (cup is too slippery)
LLM Replanning:
"Cup is slippery. Alternative plan:
1. Get paper towel
2. Dry cup
3. Pick up cup with better grip"
```
**Evaluation**
- **Success Rate**: What percentage of plans achieve the goal?
- **Efficiency**: How many actions does the plan require?
- **Robustness**: Does the plan handle unexpected situations?
- **Generalization**: Does the planner work on novel tasks?
**LLMs vs. Classical Planning**
- **Classical Planning**:
- Pros: Guarantees correctness, handles complex constraints, optimal solutions.
- Cons: Requires formal specifications, limited to predefined action spaces.
- **LLM Planning**:
- Pros: Natural language interface, common sense, flexible, handles novel tasks.
- Cons: No correctness guarantees, may generate infeasible plans.
- **Best Practice**: Combine both — LLM for high-level reasoning, classical planner for correctness.
**Benefits**
- **Natural Language Interface**: Users specify goals in plain language.
- **Common Sense**: LLMs bring real-world knowledge to planning.
- **Flexibility**: Adapts to new tasks without reprogramming.
- **Rapid Prototyping**: Quickly generate plans for testing.
**Limitations**
- **No Guarantees**: Plans may be incorrect or infeasible.
- **Grounding Gap**: High-level plans need translation to low-level actions.
- **Context Limits**: LLMs have limited context — may not track complex state.
Planning with LLMs is an **emerging and promising approach** — it makes AI planning more accessible and flexible by leveraging natural language understanding and common sense, though it requires careful integration with verification and execution systems to ensure reliability.