The document discusses heuristic search strategies in artificial intelligence, highlighting their significance in solving problems more efficiently than classical methods. It outlines the historical context of heuristics, their types, applications, and limitations, emphasizing their use in decision-making with limited resources. The conclusion reinforces the importance of heuristic techniques in navigating complex problem spaces and finding practical solutions.

1. MODERN INSTITUTE OF ENGINEERING& TECHNOLOGY
NAME– RANITHALDER
SUBJECT– artificialintelligence
REGNO.– 212690100110017
ROLLNO.– 26900121016
YEAR– 3rD YEAR
SEM– 5th SEM
DEPT– COMPUTERSCIENCE
ANDENGINEERING
heuristic search strategies

2. ACKNOWLEDGMENTS
I WOULDLIKETO EXPRESS MY SPECIALTHANKS TO MY TEACHER(Aditya sir) WHOGAVE
ME THE GOLDENOPPORTUNITYTO DO THISWONDERFUL PROJECTON THE TOPIC
(heuristicsearch strategies) .WHICHHELPEDME TO DO A LOTS OF RESEARCHAND I CAME
TO KNOWABOUTSO MANYNEWTHINGS.I AM REALLYTHANKFUL TO THEM.
SECONDLY, I WOULDLIKETO THANKSMY FRIENDs & parents WHOHELPEDME IN
DOINGTHIS PROJECTWITHINLIMITEDTIMEFRAME.

3. Introduction
A heuristic is a technique that is used to solve a problem faster
than the classic methods. These techniques are used to find the
approximate solution of a problem when classical methods do
not. Heuristics are said to be the problem-solving techniques that
result in practical and quick solutions.

4. history
Psychologists Daniel Kahneman and Amos Tversky have
developed the study of Heuristics in human decision-making in
the 1970s and 1980s. However, this concept was first introduced
by the Nobel Laureate Herbert A. Simon, whose primary object
of research was problem-solving.

5. Heuristic search techniques in AI (Artificial
Intelligence)
A heuristic is a technique that is used to solve
a problem faster than the classic methods.
These techniques are used to find the
approximate solution of a problem when
classical methods do not.
Heuristics are said to be the problem-solving
techniques that result in practical and quick
solutions.

6. Heuristic Search Technique
Heuristics are strategies that are derived from past experience with
similar problems. Heuristics use practical methods and shortcuts used to
produce the solutions that may or may not be optimal, but those
solutions are sufficient in a given limited timeframe.
Heuristic Techniques into two Categories
1. Direct Heuristic Search techniques in AI
2. Weak Heuristic Search techniques in AI

7. • Direct Heuristic Search Techniques in AI
It includes Blind Search, Uninformed Search, and Blind control strategy. These search
techniques are not always possible as they require much memory and time. These techniques
search the complete space for a solution and use the arbitrary ordering of operations.
The examples of Direct Heuristic search techniques include Breadth-First Search (BFS) and
Depth First Search (DFS).
• Weak Heuristic Search Techniques in AI
It includes Informed Search, Heuristic Search, and Heuristic control strategy. These techniques
are helpful when they are applied properly to the right types of tasks. They usually require
domain-specific information.
The examples of Weak Heuristic search techniques include Best First Search (BFS) and A*.

8. Why do we need heuristics?
Heuristics are used in situations in which there is the requirement of
a short-term solution. On facing complex situations with limited
resources and time, Heuristics can help the companies to make
quick decisions by shortcuts and approximated calculations. Most of
the heuristic methods involve mental shortcuts to make decisions on
past experiences.
The heuristic method might not always provide us the finest solution,
but it is assured that it helps us find a good solution in a reasonable
time.
Based on context, there can be different heuristic methods that
correlate with the problem's scope. The most common heuristic
methods are - trial and error, guesswork, the process of elimination,
historical data analysis. These methods involve simply available
information that is not particular to the problem but is most
appropriate. They can include representative, affect, and availability
heuristics.

9. Limitation of heuristics
Along with the benefits, heuristic also has some limitations.
• Although heuristics speed up our decision-making process and
also help us to solve problems, they can also introduce errors
just because something has worked accurately in the past, so it
does not mean that it will work again.
• It will hard to find alternative solutions or ideas if we always rely
on the existing solutions or heuristics.

10. Conclusion
• In conclusion, we have learned that heuristic search strategies are an
important tool in the field of AI. By using heuristics, we can efficiently
search through large problem spaces and find optimal solutions. We
have also seen how different types of heuristic search algorithms,
such as A* search and greedy search, have their own strengths and
weaknesses depending on the problem at hand.
• That's all about the article. Hence, in this article, we have
discussed the heuristic techniques that are the problem-solving
techniques that result in a quick and practical solution. We have
also discussed some algorithms and examples, as well as the
limitation of heuristics.

11. reference
1. Pearl, Judea (1984). Heuristics: intelligent search strategies for
computer problem solving. United States: Addison-Wesley Pub. Co.,
Inc., Reading, MA. p. 3. OSTI 5127296.
2. Apter, Michael J. (1970). The Computer Simulation of Behaviour.
London: Hutchinson & Co. p. 83. ISBN 9781351021005.
3.Jon Louis Bentley (1982). Writing Efficient Programs. Prentice Hall.
p. 11.
4.Allen Newell and Herbert A. Simon (1976). "Computer Science as
Empirical Inquiry: Symbols and Search" (PDF). Comm. ACM. 19 (3):
113–126. doi:10.1145/360018.360022. S2CID 5581562.
5. "Definition of heuristic in English". Oxford University Press. Archived
from the original on 23 October 2016. Retrieved 22 October 2016.