Building Planning Systems

Planning is the heart of intelligent behavior in AI agents. It's the process of determining a sequence of actions that will transform the current state of the world into a desired goal state. In this module, we'll explore how to build sophisticated planning systems that enable agents to reason about complex, multi-step tasks and adapt their strategies when conditions change.

By the end of this module, you will:

• Understand different planning paradigms and their appropriate applications
• Implement classical planning algorithms including STRIPS and A* search
• Design hierarchical task networks for breaking down complex problems
• Build adaptive replanning systems that handle dynamic environments
• Create goal-oriented agents with flexible planning capabilities
• Integrate planning with uncertainty and partial observability

Planning is the process of finding a sequence of actions that transforms an initial state into a goal state. It involves:

1. State Representation: How we model the world
2. Action Model: What actions are available and their effects
3. Goal Specification: What we want to achieve
4. Search Strategy: How we find a solution plan

Core Components

State Space

The state space represents all possible configurations of the world. States can be:

• Atomic: Indivisible units (e.g., location names)
• Factored: Sets of variables (e.g., robot_location=kitchen, battery_level=80%)
• Structured: Complex objects with relationships

Actions

Actions are the building blocks of plans. Each action has:

• Preconditions: What must be true to execute the action
• Effects: How the action changes the world state
• Cost: Resources required for execution

Goals

Goals specify the desired end state. They can be:

• Achievement goals: Reach a specific state
• Maintenance goals: Keep something true
• Temporal goals: Accomplish within time constraints

STRIPS (Stanford Research Institute Problem Solver) is a foundational planning algorithm that represents states as sets of logical propositions.

STRIPS Representation

• States: Sets of ground propositions
• Actions: Defined by preconditions and effects
• Goals: Conjunction of propositions

Code Example: STRIPS Planner Implementation

A* Planning Algorithm

A* search is optimal when using an admissible heuristic and can be very effective for planning problems with good heuristic functions.

Code Example: A* Planning with Advanced Heuristics

Hierarchical Task Networks break complex tasks into simpler subtasks, making planning more tractable and allowing domain knowledge to guide the search.

HTN Concepts

• Primitive Tasks: Basic actions that can be executed directly
• Compound Tasks: Complex tasks that must be decomposed
• Methods: Ways to decompose compound tasks into subtasks
• Ordering Constraints: Relationships between subtasks

Code Example: HTN Planner Implementation

Real-world environments are dynamic, and plans often need to be modified during execution. Adaptive planning systems monitor execution and replan when necessary.

Replanning Strategies

1. Complete Replanning: Generate entirely new plan when failure occurs
2. Plan Repair: Modify existing plan to handle failures
3. Contingency Planning: Pre-compute plans for expected failures
4. Continuous Planning: Interleave planning and execution

Code Example: Adaptive Replanning System

Advanced agents often need to manage multiple goals simultaneously, resolve conflicts, and adapt their objectives based on changing circumstances.

Goal Management Concepts

1. Goal Types:

   • Achievement goals (reach a state)
   • Maintenance goals (keep a condition true)
   • Performance goals (optimize a metric)

2. Goal Relationships:

   • Independent goals
   • Conflicting goals
   • Hierarchical goals
   • Temporal dependencies

3. Goal Operations:

   • Goal adoption
   • Goal abandonment
   • Goal prioritization
   • Goal decomposition

Code Example: Goal Management System

Test your understanding of planning systems:

Question 1

What is the main difference between STRIPS and HTN planning?

A) STRIPS uses heuristics, HTN doesn't B) STRIPS plans in state space, HTN plans in task space
C) STRIPS is optimal, HTN is not D) STRIPS handles uncertainty, HTN doesn't

Answer: B) STRIPS plans in state space, HTN plans in task space

STRIPS planning searches through possible world states to find a sequence of actions, while HTN planning decomposes high-level tasks into subtasks hierarchically.

Question 2

Which replanning strategy is most efficient for minor plan failures?

A) Complete replanning B) Plan repair C) Contingency planning
D) Continuous planning

Answer: B) Plan repair

Plan repair attempts to fix the existing plan rather than starting from scratch, making it more efficient for minor failures where most of the plan is still valid.

Question 3

What makes A* planning optimal?

A) It uses the best heuristic function B) It explores all possible states C) It uses an admissible heuristic D) It always finds the shortest plan

Answer: C) It uses an admissible heuristic

A* is optimal when using an admissible heuristic (one that never overestimates the actual cost to reach the goal).

Time: 45 minutes

Implement an A* planner for robot navigation in a grid world with:

• Static obstacles
• Different terrain costs
• Multiple goal locations
• Efficient heuristic function

Exercise 2: Task Decomposition with HTN

Time: 60 minutes

Build an HTN planner for a complex multi-step cooking recipe that:

• Decomposes "make_dinner" into subtasks
• Handles ingredient preparation dependencies
• Manages cooking equipment constraints
• Provides multiple cooking methods

Exercise 3: Adaptive Mission Planner

Time: 90 minutes

Create an adaptive planner for a drone mission that:

• Plans routes while avoiding no-fly zones
• Replans when weather conditions change
• Manages battery life and charging stations
• Handles mission priority changes

Planning systems are essential for creating intelligent agents that can reason about complex, multi-step tasks. Key takeaways:

• Classical planning (STRIPS, A*) works well for deterministic environments
• Hierarchical planning (HTN) scales to complex domains through task decomposition
• Adaptive planning handles dynamic environments through monitoring and replanning
• Goal management enables agents to balance multiple competing objectives
• Planning with uncertainty requires probabilistic reasoning and robust strategies

The choice of planning approach depends on your domain characteristics, computational constraints, and uncertainty levels.

In the next module, we'll explore Tool Orchestration, where you'll learn how to build agents that can effectively coordinate and utilize multiple tools and services to accomplish complex tasks that require diverse capabilities.

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• Artificial Intelligence: A Modern Approach - Planning Chapters
• STRIPS Planning Algorithm
• Hierarchical Task Network Planning
• Planning Under Uncertainty
• LangChain Planning Documentation