Lec 05: Informed Search

Introduction

This tutorial will guide you through the fundamentals of informed search strategies in artificial intelligence, based on concepts discussed in the NPTEL lecture. Understanding informed search is essential for developing efficient algorithms that can solve complex problems by utilizing additional information about the problem domain.

Step 1: Understand the Basics of Search Algorithms

• What is a Search Algorithm?

  • A method for exploring possible solutions to find the best one.
  • Can be categorized into uninformed and informed search strategies.

• Difference Between Uninformed and Informed Search

  • Uninformed Search: Lacks additional information about the goal state.
  • Informed Search: Utilizes heuristics or domain-specific knowledge to improve search efficiency.

Step 2: Explore Heuristic Functions

• Definition of Heuristic Function

  • A function that estimates the cost to reach the goal from a given state.

• Characteristics of Good Heuristics

  • Should be admissible: never overestimates the cost to reach the goal.
  • Should be consistent: the estimated cost should be less than or equal to the cost from the current state to a neighbor plus the estimated cost from the neighbor to the goal.

Step 3: Learn About A* Search Algorithm

• Overview of A*

  • A popular informed search algorithm that combines features of Dijkstra’s algorithm and greedy best-first search.

• A Formula*

  • The A* algorithm uses the formula:

    f(n) = g(n) + h(n)
    

    • f(n): total estimated cost of the cheapest solution through node n
    • g(n): cost from the start node to node n
    • h(n): estimated cost from node n to the goal

• Steps to Implement A*

  1. Initialize the open list with the starting node.
  2. Loop until the open list is empty:
     • Remove the node with the lowest f(n) value.
     • If it is the goal node, backtrack to find the path.
     • Otherwise, generate its neighbors and calculate their f(n) values.
     • Add them to the open list if they are not already present or if their f(n) is lower than previously recorded.

Step 4: Practical Application of Informed Search

• Use Cases of Informed Search Algorithms

  • Pathfinding in games.
  • Robotics navigation.
  • Network routing protocols.

• Common Pitfalls to Avoid

  • Using a heuristic that is not admissible can lead to suboptimal solutions.
  • Overestimating the heuristic can cause the algorithm to miss the optimal path.

Conclusion

Informed search algorithms significantly enhance the efficiency of problem-solving in artificial intelligence by utilizing heuristic information. Understanding the key concepts, such as heuristic functions and the A* algorithm, empowers you to implement these strategies in practical applications. As a next step, consider experimenting with different heuristic functions in various scenarios to see how they affect the performance of the A* algorithm.