Introduction to Heuristic Search

Tutorial 4 of 5

Introduction to Heuristic Search

Introduction

In this tutorial, we aim to introduce you to a fundamental concept in artificial intelligence (AI): the Heuristic Search. This is a strategy used to search through the problem space where the best path is selected based on the cost function and heuristic.

By the end of this tutorial, you will understand the concept of Heuristic Search, how it works, and how to implement it in a simple program.

Prerequisites:
- Basic programming knowledge, preferably in Python.
- Basic understanding of AI and algorithms.

Step-by-Step Guide

What is Heuristic Search?

Heuristic Search is a technique in AI that is used for searching through large amounts of data where exhaustive search is impractical. It uses a heuristic function to guide the search process by providing an estimate of the optimal solution from a given state.

How does it work?

The Heuristic Search algorithm evaluates each node by combining the cost to reach the node and the cost to get from the node to the goal. The node with the lowest total cost is selected for expansion. This process is repeated until the goal node is found.

Best Practices and Tips

Always normalize your data before using a Heuristic Search.
Your heuristic function should never overestimate the cost to reach the goal.
The effectiveness of a heuristic function highly depends on the problem domain.

Code Examples

Example 1: A* Search Algorithm

The A* Search Algorithm is one of the best and popular techniques used for path finding and graph traversals.

# A* Search Algorithm in Python

import heapq

def heuristic(a, b):
    return (b[0] - a[0]) ** 2 + (b[1] - a[1]) ** 2

def astar(array, start, goal):

    neighbors = [(0,1),(0,-1),(1,0),(-1,0),(1,1),(1,-1),(-1,1),(-1,-1)]

    close_set = set()
    came_from = {}
    gscore = {start:0}
    fscore = {start:heuristic(start, goal)}
    oheap = []

    heapq.heappush(oheap, (fscore[start], start))

    while oheap:

        current = heapq.heappop(oheap)[1]

        if current == goal:
            data = []
            while current in came_from:
                data.append(current)
                current = came_from[current]
            return data

        close_set.add(current)
        for i, j in neighbors:
            neighbor = current[0] + i, current[1] + j            
            tentative_g_score = gscore[current] + heuristic(current, neighbor)
            if 0 <= neighbor[0] < array.shape[0]:
                if 0 <= neighbor[1] < array.shape[1]:                
                    if array[neighbor[0]][neighbor[1]] == 1:
                        continue
                else:
                    # array bound y walls
                    continue
            else:
                # array bound x walls
                continue

            if neighbor in close_set and tentative_g_score >= gscore.get(neighbor, 0):
                continue

            if  tentative_g_score < gscore.get(neighbor, 0) or neighbor not in [i[1]for i in oheap]:
                came_from[neighbor] = current
                gscore[neighbor] = tentative_g_score
                fscore[neighbor] = tentative_g_score + heuristic(neighbor, goal)
                heapq.heappush(oheap, (fscore[neighbor], neighbor))

    return False

This code implements the A* Search algorithm, which uses a heuristic to estimate the cost from the current node to the goal. This heuristic helps guide the search process to the most promising node.

Summary

We introduced Heuristic Search and its application in AI.
We explored how the A* Search algorithm works, with a practical code example.
We discussed best practices and tips when using Heuristic Search.

Next Steps

You can extend your learning by diving into different types of heuristic search algorithms like Best-First Search, Hill Climbing, Beam Search, etc.

Practice Exercises

Implement a Heuristic Search to solve the 8-puzzle problem.
Use a Heuristic Search to find the shortest path in a maze.
Compare the performance of the A* Search with other search algorithms.

Remember, practice is key to mastering any concept. Happy learning!