✦Sparkl
innovator · thinks

Search & Planning

Master AI search algorithms and planning strategies by building agents that solve real-world logistics problems. Learn how machines navigate state spaces and make intelligent decisions.

20 modules·Difficulty: ★★★★☆· 5 Free
Start module 1

Modules

  • 1
    Free8 min
    What Is a State Space?
    Discover how AI agents represent problems as graphs of possible states and transitions 🗺️
  • 2
    Free10 min
    Actions, Costs, and Goal States
    Learn how agents define valid moves, assign costs, and recognize when they have reached their goal
  • 3
    Free12 min
    Breadth-First Search Fundamentals
    Explore the first uninformed search algorithm that guarantees finding the shortest path
  • 4
    Free11 min
    Depth-First Search and Backtracking
    Understand how depth-first search explores deeply before backtracking to find solutions
  • 5
    Free9 min
    Comparing Search Strategies
    Compare uninformed algorithms using time, space, completeness, and optimality metrics
  • 6
    Paid10 min
    Uniform Cost Search
    Build a search algorithm that always expands the lowest-cost path first for optimal solutions
  • 7
    Paid12 min
    Introduction to Heuristics
    Learn how domain knowledge guides search toward the goal more efficiently
  • 8
    Paid11 min
    Greedy Best-First Search
    Implement a fast but non-optimal algorithm that always moves toward the most promising state
  • 9
    Paid10 min
    A* Search Algorithm
    Master the gold-standard algorithm combining path cost and heuristic estimates for optimal search
  • 10
    Paid9 min
    Admissibility and Consistency
    Understand the mathematical properties heuristics must have to guarantee optimal A* solutions
  • 11
    Paid12 min
    Designing Manhattan Distance Heuristics
    Create grid-based heuristics using taxicab geometry for pathfinding problems
  • 12
    Paid11 min
    Euclidean and Diagonal Distance Metrics
    Extend heuristic design to environments allowing diagonal movement and continuous spaces
  • 13
    Paid10 min
    Memory-Bounded Search
    Tackle large state spaces using iterative deepening and memory-efficient variants of A*
  • 14
    Paid9 min
    Constraint Satisfaction Problems
    Model scheduling and allocation tasks as variables with domain constraints to satisfy
  • 15
    Paid12 min
    Backtracking Search for CSPs
    Implement systematic search with pruning techniques to solve constraint problems efficiently
  • 16
    Paid11 min
    Planning Domain Definition Language
    Express complex planning problems using formal action schemas with preconditions and effects
  • 17
    Paid10 min
    Forward and Backward Planning
    Compare progression from initial state versus regression from goal to build action sequences
  • 18
    Paid12 min
    Multi-Agent Coordination
    Coordinate multiple planners working toward shared goals while avoiding conflicts
  • 19
    Paid11 min
    Capstone: Delivery Route Planner
    Build a complete logistics agent that routes packages using A* search with custom heuristics
  • 20
    Final exam8 min
    Search & Planning Mastery Exam
    Demonstrate your understanding of search algorithms, heuristics, and planning techniques