Advanced Planning: Goal Decomposition and Uncertainty

Overview

Think about how a master chef plans a complex dinner party. They don't just start cooking—they work backwards from the goal, breaking down the feast into individual dishes, then into preparation steps, considering timing constraints, ingredient availability, and potential problems like equipment failures or missing ingredients. They create contingency plans: "If the oven breaks, I'll use the grill for the roast and adjust the menu accordingly."

This sophisticated approach to planning—decomposing complex goals and handling uncertainty—is what separates expert planners from novices. In AI agents, these capabilities enable robust performance in real-world scenarios where goals are complex, resources are uncertain, and unexpected events can derail even the best-laid plans.

Learning Objectives

After completing this lesson, you will be able to:

• Apply means-ends analysis to decompose complex goals systematically
• Design goal decomposition strategies for different types of planning problems
• Handle planning under uncertainty with probabilistic outcomes
• Create robust contingency plans that handle multiple failure modes
• Implement dynamic replanning systems that adapt to changing conditions

Goal Decomposition Strategies

Means-Ends Analysis

Means-ends analysis is a powerful technique for breaking down complex goals by identifying differences between current and desired states and finding actions to reduce those differences.

The Process:

1. Compare current state to goal state
2. Identify the differences
3. Find operators (actions) that reduce those differences
4. Apply operators and repeat

Interactive Goal Decomposition Visualization

Goal Decomposition Comparison

Hierarchical Task Networks (HTN)

HTN planning breaks down high-level goals into manageable subtasks through recursive decomposition:

HTN Advantages:

• Natural decomposition: Mirrors human problem-solving
• Flexible planning: Multiple decomposition methods per task
• Domain knowledge: Incorporates expert knowledge about task structure
• Scalable: Works for both simple and complex problems

Interactive HTN Planning Demo

Planning Under Uncertainty

Real-world planning must handle uncertainty in action outcomes, environmental changes, and resource availability.

Uncertainty Types Visualization

Probabilistic Planning Strategies

Practice Exercises

Exercise 1: Means-Ends Analysis Enhancement

Extend the means-ends analysis system:

1. Add more sophisticated difference prioritization
2. Implement backtracking when subgoals conflict
3. Add learning from failed decomposition attempts
4. Create visualizations of the goal decomposition process

Exercise 2: Multi-Objective Planning

Build a planner that handles multiple competing objectives:

1. Design trade-off mechanisms between time, cost, and reliability
2. Implement Pareto-optimal planning
3. Add user preference learning
4. Create decision support for objective weighting

Exercise 3: Dynamic Uncertainty

Create a planning system that adapts to changing uncertainty:

1. Update action outcome probabilities based on experience
2. Implement online replanning when predictions fail
3. Add confidence intervals for probability estimates
4. Create adaptive contingency planning

Exercise 4: Real-World Domain

Apply advanced planning to a complex real-world domain:

1. Model a software deployment pipeline with multiple failure modes
2. Create meal planning with dietary constraints and uncertain ingredient availability
3. Design emergency response planning with resource uncertainty
4. Compare different planning approaches on the same domain

Looking Ahead

In our next lesson, we'll return to Advanced Reasoning and Self-Reflection. We'll learn how agents can:

• Evaluate their own planning decisions and outcomes
• Learn from planning failures to improve future performance
• Adapt their planning strategies based on domain characteristics
• Integrate planning with meta-cognitive capabilities

The advanced planning techniques we've built will provide the foundation for sophisticated reasoning agents that can not only make robust plans but also reflect on and improve their planning processes.

Further Reading

Papers & Articles

• Self-Discover: Large Language Models Self-Compose Reasoning Structures — LLMs selecting and composing reasoning modules for each task
• Reflexion: Language Agents with Verbal Reinforcement Learning — agents that reflect on failures to improve planning
• Graph of Thoughts: Solving Elaborate Problems with LLMs — modeling reasoning as an arbitrary graph for complex planning
• Planning under Uncertainty Survey — comprehensive survey of probabilistic planning methods
• Probabilistic Planning Algorithms (POMDP Tutorial) — tutorial on partially observable Markov decision processes