Artificial Intelligence : Unit - 2 Part - 5 : Search with Partial Information (Heuristic Search)

 

UNIT - II

Topic: Search with Partial Information (Heuristic Search)

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Introduction

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What is Heuristic Search?

Heuristic Search is an informed search strategy that uses additional knowledge (called a heuristic) to make better decisions during search.

Unlike uninformed (blind) search methods like BFS and DFS, which explore the search space blindly, heuristic search tries to guess which path is more promising by using a heuristic function.

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Key Concepts:

·        Heuristic Function:

A function that estimates the cost of reaching the goal from a given state. It provides guidance to the search algorithm without guaranteeing an optimal path, but it significantly speeds up the search process. 

·        Informed Search:

Heuristic search is an informed search strategy because it uses additional knowledge (the heuristic function) to guide the search, unlike uninformed search methods like Breadth-First Search or Depth-First Search which explore all possibilities without any guidance. 

·        State Space:

The set of all possible states in a problem. Heuristic search explores this space to find a solution. 

·        Computational Complexity:

Heuristic search aims to reduce the computational cost of finding a solution by intelligently exploring the search space. It balances exploration (searching new possibilities) and exploitation (using known solutions). 

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Example of a Heuristic

• In a map-based search, the straight-line distance (Euclidean distance) from a city to the destination is a heuristic.
• It’s not the actual road distance but helps estimate which city is closer to the goal.

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Need for Heuristic Search

• Uninformed search algorithms explore all possible paths, which is slow and memory-intensive.
• Heuristic search focuses only on promising paths, reducing time and space requirements.
• It is useful when:
• The search space is large
• The goal is far
• The agent has partial information

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Benefits of Heuristic Search:

·        Efficiency:

Reduces the number of states that need to be explored, leading to faster solutions, especially for large state spaces.

·        Scalability:

Can handle complex problems that would be intractable for uninformed search methods.

·        Practical Applications:

Widely used in navigation systems, game playing, optimization problems, and other AI applications. 

Limitations:

·        Heuristic Accuracy:

The effectiveness of heuristic search depends heavily on the quality of the heuristic function. A poorly designed heuristic can lead to suboptimal solutions or even fail to find a solution. 

·        Local Optima:

Some heuristic algorithms, like Hill Climbing, can get stuck in local optima, meaning they find a good solution but not the best possible solution. 

·        Completeness and Optimality:

While some heuristic algorithms (like A*) can be complete and optimal under certain conditions, others may not guarantee finding the best solution or finding a solution at all. 

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 Types of Heuristic Search Algorithms

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1. Greedy Best-First Search

2. A Search Algorithm*

3. Hill Climbing Search

4. AO* Algorithm (And-Or Graph Search)

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📝 Summary

• Heuristic search uses extra knowledge to find solutions more efficiently.
• A heuristic is a rule-of-thumb that estimates the closeness to goal.
• Popular heuristic algorithms include:
• Greedy Search (fast, not optimal)
• A* Search (optimal and complete)
• Hill Climbing (simple but risky)
• AO* (used for complex task reduction)
• Choosing a good heuristic function is key to successful problem solving in AI.

"Heuristic search is like using your brain while exploring — not just walking blindly!"