Heuristic Search Techniques in Artificial Intelligence

Heuristic
Search
Techniques
-Dr. Mehak Saini

Blind search techniques
Heuristic search
techniques
do not use any
information about the
problem domain and
explore the search space
blindly.
use additional information,
such as a heuristic
function, to make
informed decisions during
the search process.
Examples include
**Breadth-First Search
(BFS)** and **Depth-First
Search (DFS)**, where the
search follows a
predetermined order
without any guidance.
Examples include **A***
and **Greedy Search**,
which rely on heuristic
values to estimate the
most promising paths.
A heuristic search technique is a search method that uses a heuristic function to
guide the search process. The heuristic function provides an estimate of how close a
given state is to the goal, helping the algorithm make decisions that potentially lead to
faster solutions.

Generate & Test
Algorithm
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Hill Climbing
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2 8 3
1 6 4
7 5
1 2 3
8 4
7 6 5
Consider trying to solve the 8-puzzle using hill climbing. Can you find a heuristic function that makes this work? Make sure it
works on the following example:
Start State (S) Goal State (G)

What can be the
heuristic function in this
example?
1.) Manhattan distance
2.) Hamming distance

Problems with the hill climbing method and their solutions:
1. Local maxima:
In some cases, the algorithm might reach a local maximum, which is a solution that is better than its
neighbors but not the best possible solution (global maximum).
Solution: One way to address this problem is by using random restarts, where the algorithm is
restarted from different initial points to explore other parts of the search space and avoid getting
stuck in local maxima.
2. Plateaus:
A plateau is an area of the search space where many neighboring states have the same value,
causing the algorithm to get "stuck" without making progress.
Solution: Simulated annealing or random walks can be used to allow the algorithm to explore other
parts of the search space even when no immediate improvement is found.
3. Ridges:
A ridge is a steep area where progress requires moving sideways before moving upward. Hill climbing
may fail to make this move, getting stuck in suboptimal states.
Solution: Using stochastic hill climbing (randomized moves) or genetic algorithms can help traverse
ridges by allowing non-greedy moves that explore other solutions.
These solutions help overcome the limitations of the hill climbing method by introducing more flexibility
in the search process.

Advantages of Hill Climbing algorithm
1 It is a simple and intuitive algorithm that is easy to understand and
implement.
2 It can be used in a wide variety of optimization problems, including those
with a large search space and complex constraints.
3 It is often very efficient in finding local optima, making it a good choice for
problems where a good solution is needed quickly.
4 The algorithm can be easily modified and extended to include additional
heuristics or constraints.

[ii] Best first search and A* search
Best first search is a general search algorithm that selects the next node to expand based on a heuristic function (h(n)), which
estimates the cost to reach the goal from that node. A* search is a specific type of best first search that uses both the cost from
the start to the current node (g(n)) and the heuristic estimate (h(n)), combining them as (f(n) = g(n) + h(n)), ensuring optimality if
the heuristic is admissible.

Thus,
A* algorithm combine the best features of both Best First
Search and Branch and Bound, (B&B) algorithms.

Find the best route from the start node S to the goal node G in the graph given below, using A* search algorithm.

Solve the tree below using the A* algorithm. Consider 'Start' as the start node and
'Goal' as the destination node. Use the provided heuristic values for each node.

Concept of game playing in Artificial Intelligence
Game playing in Artificial Intelligence refers to the creation of AI
systems that can play and solve competitive games, such as chess,
checkers, or Go. These systems involve decision-making, planning, and
strategizing to maximize a player's chance of winning by predicting
future states of the game, often through search techniques like
minimax and using heuristics to evaluate positions.

One widely used algorithm in AI for game playing is the **Minimax Algorithm**:
- **Minimax** is a recursive algorithm used for decision making in two-player games. The goal is to
minimize the possible loss for a worst-case scenario. One player is the maximizer, trying to
maximize their score, while the other player (the minimizer) tries to minimize the maximizer's score.
- **Working of Minimax**: It simulates all possible moves in a game and evaluates the outcome of
each move. The maximizer looks for the maximum possible score from its moves, while the
minimizer looks for the minimum possible score. The algorithm recursively explores the game tree
until it reaches a terminal state (win/loss/draw).
- In each state, the algorithm backtracks the evaluation from leaf nodes to the root, where the
final decision is made based on maximizing the player's advantage while minimizing the opponent’s
advantage.

Alpha and beta cutoffs on Minmax search strategy
Alpha and beta cutoffs are optimization techniques used in the minimax algorithm to prune branches of the search tree that are
not going to affect the final decision. Alpha represents the best score that the maximizing player can guarantee, and beta
represents the best score that the minimizing player can guarantee. If a certain move is found to be worse than a previously
examined one, further exploration of that branch is stopped.

Heuristic Search Techniques in Artificial Intelligence

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