This document discusses heuristic search techniques in artificial intelligence. It begins by defining a heuristic as a technique to solve problems faster than classic methods or find approximate solutions when exact methods cannot be used. It then describes direct/uninformed search techniques like breadth-first and depth-first search that examine the entire state space. Weak/informed heuristic search techniques use domain knowledge to compute preferences among child nodes and associate each node with a heuristic function. The document uses the 8-puzzle problem as an example, explaining the goal is to rearrange numbered tiles into order using horizontal and vertical slides of tiles into the blank space. Finally, it outlines advantages like early feedback and disadvantages like requiring expert knowledge and experience to apply heuristics effectively.

1. Heuristic Search
Prof. Neeraj Bhargava
Kapil Chauhan
Department of Computer Science
School of Engineering & Systems Sciences
MDS University, Ajmer

2. Content’s
 Introduction
 Techniques
 Statement
 8 Puzzel
 Advantages - Disadvantages

3. Heuristic Search
1) A Heuristic is a technique to solve a problem faster
than classic methods, or to find an approximate
solution when classic methods cannot.
2) This is a kind of a shortcut as we often trade one of
optimality, completeness, accuracy, or precision for
speed.

4. Heuristic Search Techniques in Artificial
Intelligence

5. Direct Heuristic Search Techniques in AI
Other names for these are Blind Search, Uninformed
Search, and Blind Control Strategy.
These aren’t always possible since they demand much
time or memory. They search the entire state space for a
solution and use an arbitrary ordering of operations.
Examples of these are Breadth First Search (BFS) and
Depth First Search (DFS).

6. Weak Heuristic Search Techniques in AI
 Other names for these are Informed Search, Heuristic
Search, and Heuristic Control Strategy.
 These are effective if applied correctly to the right types of
tasks and usually demand domain-specific information.
 We need this extra information to compute preference
among child nodes to explore and expand. Each node has a
heuristic function associated with it.

7. Statement for 8 Puzzel problem
 8 puzzel is Square track in which are placed 8square tiles .
 The remain 9th square are uncovered , each tile has a number on
it, tile that is adjutant to blank space can be adjust into that space.
 A game consist of starting position and a specified goal position ,
the goal is to transform the starting position into the goal position
by sliding the tiles around.
 For understand the heuristic we can take an example in which
heuristic function is used in Example i.e. 8 Puzzle

8. 8 Puzzel problem
 The 8-puzzle problem is a puzzle invented and popularized by Noyes
Palmer Chapman in the 1870s.
 It is played on a 3-by-3 grid with 8 square blocks labeled 1 through 8
and a blank square.
 Our goal is to rearrange the blocks so that they are in order. You are
permitted to slide blocks horizontally or vertically into the blank square.

9. Solution

10. ADVANTAGES DISADVANTAGES
 It can provide some quick and
relatively inexpensive feedback to
designers.
 You can obtain feedback early in the
design process.
 Assigning the correct heuristic can
help suggest the best corrective
measures to designers.
 You can use it together with other
usability testing methodologies.
 You can conduct usability testing to
further examine potential issues.
 It requires knowledge and
experience to apply the heuristics
effectively.
 Trained usability experts are
sometimes hard to find and can be
expensive.
 You should use multiple experts and
aggregate their results.
 The evaluation may identify more
minor issues and fewer major issues.

11. Assignment
 Explain Heuristic search in deep learning problem
with suitable example.