AI Search Techniques Explained

1. Introduction

This tutorial aims to introduce you to AI search techniques, a fundamental aspect of AI that empowers systems to solve complex problems. By the end of this tutorial, you will understand the basic principles behind these techniques and how they're used in AI.

Prerequisites: Basic understanding of programming and algorithms.

2. Step-by-Step Guide

AI search techniques are algorithms that traverse through a search space (possible solutions) to find a solution to a problem. They are broadly divided into two types: Uninformed Search (Blind Search) and Informed Search (Heuristic Search).

Uninformed Search: This technique does not have any additional information about the search space except its structure. Examples include Breadth-First Search and Depth-First Search.

Informed Search: This technique uses heuristic function to provide information about the search space. Examples include Greedy Best-First Search and A* Search.

Breadth-First Search (BFS)

BFS is a simple strategy where you start from the root and traverse the tree layer by layer.

from collections import deque

def bfs(graph, root):
    visited, queue = set(), deque([root])
    visited.add(root)
    while queue:
        vertex = queue.popleft()
        print(str(vertex) + " ", end="")
        for neighbour in graph[vertex]:
            if neighbour not in visited:
                visited.add(neighbour)
                queue.append(neighbour)

In the above code, deque is a double-ended queue from Python's collections module. BFS uses a queue data structure to keep track of nodes that it needs to explore.

A* Search

A* Search algorithm uses a heuristic to estimate the cost from current node to the goal, which helps in finding the shortest path.

from queue import PriorityQueue

def a_star_search(graph, start, goal):
    queue = PriorityQueue()
    queue.put(start, 0)
    came_from = {start: None}
    cost_so_far = {start: 0}

    while not queue.empty():
        current = queue.get()

        if current == goal:
            break

        for next in graph.neighbors(current):
            new_cost = cost_so_far[current] + graph.cost(current, next)
            if next not in cost_so_far or new_cost < cost_so_far[next]:
                cost_so_far[next] = new_cost
                priority = new_cost + graph.heuristic(next, goal)
                queue.put(next, priority)
                came_from[next] = current
    return came_from, cost_so_far

In the above code, PriorityQueue is a queue data structure where the element with highest priority is dequeued first.

3. Summary

In this tutorial, we have covered the basics of AI Search Techniques, including Uninformed and Informed Search. We also looked at examples of both techniques, namely Breadth-First Search and A* Search.

Next, you can practice these techniques with different problems and explore other search techniques such as Depth-First Search, Greedy Best-First Search, and so on.

4. Practice Exercises

1. Implement a Depth-First Search algorithm.
2. Implement a Greedy Best-First Search algorithm.
3. Compare these techniques in terms of their time and space complexity.

Remember, the key to mastering these techniques is consistent practice. Happy coding!