Hill Climbing Algorithm in Artificial Intelligence with Real Life Examples| Heuristic Search

Summary

This video introduces the Simple Hill Climbing algorithm, a local search technique with no global problem knowledge. It explores the greedy approach, where the algorithm keeps moving forward as long as it finds a better state, stopping when no improvements are possible. Key points covered include local maxima, plateaus, and ridges—common problems that can limit progress. Using examples, the video explains how the algorithm selects the best move based on heuristic values and highlights the challenges faced when the algorithm cannot backtrack. It's a valuable resource for students preparing for exams.

Takeaways

• 🔍 Simple hill climbing is a local search algorithm with knowledge limited to its local domain, not the global problem.
• ⚡ The algorithm uses a greedy approach, continuously progressing until the best move is no longer available.
• 🚫 Hill climbing does not allow backtracking, meaning it cannot reverse once it reaches a point where no better move is possible.
• 🌲 The algorithm starts from an initial state and explores new nodes by selecting the best from multiple branches based on their heuristic values.
• 🧮 Beam width in hill climbing is 1, meaning it explores only the best move and forgets the others without saving them in memory.
• 🧗 The algorithm mimics climbing a hill while blindfolded, moving upward as long as a better state is found but stopping once no better state is available.
• 📉 The local maximum problem occurs when the algorithm reaches a peak that is not globally optimal, preventing further improvement.
• ⛰️ A plateau or flat maximum occurs when all neighboring states have the same heuristic value, causing the algorithm to stop due to no clear direction.
• 🧗‍♂️ Ridge is a problem in hill climbing where the algorithm moves in only one direction, even if a better state is available in another direction.
• ❌ Hill climbing is efficient in terms of space complexity since it does not store all states, only the best current state, but this can lead to issues like local maxima.

Q & A

What is the simple hill climbing algorithm?

The simple hill climbing algorithm is a local search algorithm that focuses on finding the best move by comparing the current state with neighboring states, aiming to achieve a better state without knowledge of the global solution.

What does it mean that the simple hill climbing algorithm is a local search algorithm?

It means that the algorithm only has knowledge of its immediate surroundings (local domain) and makes decisions based solely on this information without considering the global view of the problem.

How does the greedy approach work in hill climbing?

In the greedy approach, the algorithm continuously moves towards a better state as long as it finds one. Once no better state is found, the algorithm stops.

Why is backtracking not allowed in the simple hill climbing algorithm?

Backtracking is not allowed because the algorithm does not store previously visited states in memory. Once it moves to a new state, it cannot go back, even if no better state is found.

What is the role of heuristic values in the simple hill climbing algorithm?

Heuristic values guide the algorithm to choose the best option by evaluating the cost or value of each state. The goal is to minimize the heuristic value to find the optimal solution.

What is the 'local maximum' problem in hill climbing?

The local maximum problem occurs when the algorithm reaches a state that seems like the best solution based on its local knowledge, but there may be a better solution (global maximum) beyond its current view.

What is the 'plateau' or 'flat maximum' problem in hill climbing?

The plateau problem arises when the algorithm reaches a state where all neighboring states have the same heuristic value. Since there is no better move, the algorithm cannot decide where to go next and stops.

What is the 'ridge' problem in hill climbing?

The ridge problem occurs when the algorithm is moving in a single direction and reaches a point where a better state exists in a different direction. However, because the algorithm does not change its direction, it cannot reach the better state.

How does space complexity affect the performance of the simple hill climbing algorithm?

The space complexity is low in the simple hill climbing algorithm because it does not store all the explored states in memory. It only keeps track of the current best state, which reduces memory usage.

What is the significance of 'beam width' in the hill climbing algorithm?

In simple hill climbing, the beam width is set to one, meaning the algorithm only explores the best move from the current state and discards other options. This narrow focus helps reduce complexity but limits exploration of alternative solutions.