Heuristic search techniques use heuristics or rules of thumb to help find approximate solutions faster when classic problem-solving methods are too slow or cannot solve a problem. Some common heuristic search techniques described in the document include hill climbing, simulated annealing, A* search, and best-first search. Heuristics help guide the search process by evaluating information at each step and choosing which path or branch to follow next based on ranking alternatives. While heuristic methods may not guarantee an optimal solution, they can help solve problems more efficiently than uninformed search techniques.
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
This presentation discuses the following topics:
What is A-Star (A*) Algorithm in Artificial Intelligence?
A* Algorithm Steps
Why is A* Search Algorithm Preferred?
A* and Its Basic Concepts
What is a Heuristic Function?
Admissibility of the Heuristic Function
Consistency of the Heuristic Function
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
This presentation discuses the following topics:
What is A-Star (A*) Algorithm in Artificial Intelligence?
A* Algorithm Steps
Why is A* Search Algorithm Preferred?
A* and Its Basic Concepts
What is a Heuristic Function?
Admissibility of the Heuristic Function
Consistency of the Heuristic Function
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...vikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...vikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
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# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
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What is artificial intelligence,Hill Climbing Procedure,Hill Climbing Procedure,State Space Representation and Search,classify problems in AI,AO* ALGORITHM
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...vikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do.
The method of solving problem through AI involves the process of defining the search space, deciding start and goal states and then finding the path from start state to goal state through search space.
State space search is a process used in the field of computer science, including artificial intelligence(AI), in which successive configurations or states of an instance are considered, with the goal of finding a goal state with a desired property.
I.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE II. HEURISTIC FUNCTION IN AI III...vikas dhakane
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
What is artificial intelligence,Hill Climbing Procedure,Hill Climbing Procedure,State Space Representation and Search,classify problems in AI,AO* ALGORITHM
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
How can you deal with Fuzzy Logic. Fuzzy logic is a form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree
between 0 and 1
Problem-Solving Strategies in Artificial Intelligence" delves into the core techniques and methods employed by AI systems to address complex problems. This exploration covers the two main categories of search strategies: uninformed and informed, revealing how they navigate the solution space. It also investigates the use of heuristics, which provide a shortcut for guiding the search, and local search algorithms' role in tackling optimization problems. The description offers insights into the critical concepts and strategies that power AI's ability to find solutions efficiently and effectively in various domains.
In "Problem-Solving Strategies in Artificial Intelligence," we dive deeper into the foundational techniques and methodologies that AI systems rely on to tackle challenging problems. This comprehensive exploration begins with an in-depth examination of search strategies. Uninformed search strategies, often referred to as blind searches, are dissected, along with informed search strategies that harness domain-specific knowledge and heuristics to guide the search process more intelligently.
The role of heuristics in AI problem-solving is thoroughly investigated. These problem-solving techniques employ domain-specific rules of thumb to estimate the quality of potential solutions, aiding in decision-making and prioritization. The famous A* search algorithm, which combines actual cost and heuristic estimation, is highlighted as a prime example of informed search.
Local search algorithms, another critical component, are discussed in the context of optimization problems. These algorithms excel in finding the best solution within a local neighborhood of the current solution and are particularly valuable for various optimization challenges. You'll explore methods like hill climbing and simulated annealing, which are vital for optimizing solutions in constrained problem spaces.
This insightful exploration provides a comprehensive understanding of the problem-solving strategies employed in AI, offering a solid foundation for those seeking to apply AI techniques to real-world challenges and further the field of artificial intelligence.
this is ppt on the topic of heuristic search techniques or we can also known it by the name of informed search techniques.
in this presentation we only disscuss about three search techniques there are lot of them by the most important once are in this presentation.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
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Introduction to AI for Nonprofits with Tapp NetworkTechSoup
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Biological screening of herbal drugs: Introduction and Need for
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Antifertility, Toxicity studies as per OECD guidelines
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Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
3. WHAT IS A HEURISTIC SEARCH?
• A Heuristic is a technique to solve a problem faster than classic
methods, or to find an approximate solution when classic methods
cannot.
• A Heuristic (or a heuristic function) takes a look at search algorithms. At
each branching step, it evaluates the available information and makes a
decision on which branch to follow.
• It does so by ranking alternatives. The Heuristic is any device that is
often effective but will not guarantee work in every case.
• This is a kind of a shortcut as we often trade one of optimality,
completeness, accuracy, or precision for speed. 4/4/2023 3
5. WHY DO WE NEED HEURISTICS?
• To produce a solution , in a reasonable amount of time. It
doesn’t have to be the best- an approximate solution will do
since this is fast enough.
• Reduce the polynomial number for most problems that are
exponential. And in situations where we can’t find known
algorithms.
• Heuristic Techniques may be weak methods because they
are vulnerable to combinatorial explosion. 4/4/2023 5
6. • 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).
DIRECT HEURISTIC SEARCH TECHNIQUES IN AI
4/4/2023 6
7. 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.
• Examples are Best First Search (BFS) and A*.
• Best-First Search
• A* Search
• Bidirectional Search
• Tabu Search
• Beam Search
• Simulated Annealing
• Hill Climbing
• Constraint Satisfaction Problems
4/4/2023 7
9. HILL CLIMBING – ANOTHER EXAMPLE
• Problem: You have just arrived in Washington, D.C. You’re in your car, trying to get
downtown to the Washington Monument.
4/4/2023 9
10. FEATURES OF HILL CLIMBING IN AI
• Generate and Test variant: Hill Climbing is the variant of
Generate and Test method. The Generate and Test method
produce feedback which helps to decide which direction to
move in the search space.
• Greedy approach: Hill-climbing algorithm search moves in
the direction which optimizes the cost.
• No backtracking: It does not backtrack the search space, as it
does not remember the previous states.
4/4/2023 10
11. PROBLEMS WITH HILL CLIMBING IN AI
Three issues Addressed
• Local Maximum- All neighboring states have values worse than the current. The
greedy approach means we won’t be moving to a worse state. This terminates the
process even though there may have been a better solution. As a workaround, we
use backtracking.
• Plateau- All neighbors to it have the same value. This makes it impossible to choose
a direction. To avoid this, we randomly make a big jump.
• Ridge- At a ridge, movement in all possible directions is downward. This makes it
look like a peak and terminates the process. To avoid this, we may use two or more
rules before testing.
4/4/2023 11
13. GENERATE AND TEST SEARCH
• Is a heuristic search technique based on Depth First Search with Backtracking
which guarantees to find a solution if done systematically and there exists a
solution.
• In this technique, all the solutions are generated and tested for the best solution.
• It ensures that the best solution is checked against all possible generated solutions.
• It is also known as British Museum Search Algorithm as it’s like looking for an
exhibit at random or finding an object in the British Museum by wandering
randomly.
4/4/2023 13
14. GENERATE AND TEST SEARCH
Step:1 Generate a possible solution. For
example, generating a particular point in
the problem space or generating a path for
a start state.
Step:2Test to see if this is a actual solution
by comparing the chosen point or the
endpoint of the chosen path to the set of
acceptable goal states
Step:3 If a solution is found, quit.
Otherwise go to Step 1
4/4/2023 14
16. SIMPLE HILL CLIMBING
• Examines one neighboring node at a time and selects the first one that optimizes the
current cost to be the next node.
• Algorithm:
1. Evaluate initial state- if goal state, stop and return success. Else, make initial state
current.
2. Loop until the solution reached or until no new operators left to apply to current
state:
a. Select new operator to apply to the current producing new state.
b. Evaluate new state:
• If a goal state, stop and return success.
• If better than the current state, make it current state, proceed.
• Even if not better than the current state, continue until the solution
reached.
3. Exit.
4/4/2023 16
17. FEATURES:
• Less time consuming
• Less optimal solution and the solution is not
guaranteed
4/4/2023 17
18. STEEPEST-ASCENT HILL CLIMBING:
• The steepest-Ascent algorithm is a variation of simple hill climbing algorithm. This
algorithm examines all the neighboring nodes of the current state and selects one
neighbor node which is closest to the goal state. This algorithm consumes more time
as it searches for multiple neighbors
4/4/2023 18
19. ALGORITHM FOR STEEPEST-ASCENT HILL
CLIMBING
• Step 1: Evaluate the initial state, if it is goal state then return success and stop, else
make current state as initial state.
• Step 2: Loop until a solution is found or the current state does not change.
• Let SUCC be a state such that any successor of the current state will be better than it.
• For each operator that applies to the current state:
• Apply the new operator and generate a new state.
• Evaluate the new state.
• If it is goal state, then return it and quit, else compare it to the SUCC.
• If it is better than SUCC, then set new state as SUCC.
• If the SUCC is better than the current state, then set current state to SUCC.
• Step 3: Exit.
4/4/2023 19
20. ANNEALING
• Annealing is a thermal process for obtaining low energy
states of a solid in a heat bath.
• The process contains two steps:
• Increase the temperature of the heat bath to a maximum value at
which the solid melts.
• Decrease carefully the temperature of the heat bath until the
particles arrange themselves in the ground state of the solid.
Ground state is a minimum energy state of the solid.
• The ground state of the solid is obtained only if the
maximum temperature is high enough and the cooling is
done slowly.
4/4/2023 20
21. SIMULATED ANNEALING
• Simulated annealing maintains a current assignment of values to variables.
• At each step, it picks a variable at random, then picks a value at random. If
assigning that value to the variable is an improvement or does not increase the
number of conflicts, the algorithm accepts the assignment and there is a new
current assignment.
• Otherwise, it accepts the assignment with some probability, depending on the
temperature and how much worse it is than the current assignment. If the change is
not accepted, the current assignment is unchanged.
4/4/2023 21
22. • To control how many worsening steps are accepted, there is a positive real-valued
temperature T.
• Suppose A is the current assignment of a value to each variable. Suppose that h(A) is
the evaluation of assignment A to be minimized.
• For solving constraints, h is typically the number of conflicts. Simulated annealing
selects a neighbor at random, which gives a new assignment A'. If h(A') ≤ h(A), it
accepts the assignment and A' becomes the new assignment. Otherwise, the
assignment is only accepted randomly with probability
• e(h(A)-h(A'))/T.
• Thus, if h(A') is close to h(A), the assignment is more likely to be accepted. If the
temperature is high, the exponent will be close to zero, and so the probability will be
close to 1. As the temperature approaches zero, the exponent approaches -∞, and the
probability approaches zero.
4/4/2023 22
24. OR GRAPHS
• BFS uses the concept of a Priority queue and heuristic search.
• To search the graph space, the BFS method uses two lists for tracking
the traversal.
• An ‘Open’ list that keeps track of the current ‘immediate’ nodes
available for traversal and a ‘CLOSED’ list that keeps track of the
nodes already traversed.
4/4/2023 24
25. BEST FIRST SEARCH ALGORITHM
• Create 2 empty lists: OPEN and CLOSED
• Start from the initial node (say N) and put it in the ‘ordered’ OPEN list
• Repeat the next steps until the GOAL node is reached
• If the OPEN list is empty, then EXIT the loop returning ‘False’
• Select the first/top node (say N) in the OPEN list and move it to the CLOSED list. Also,
capture the information of the parent node
• If N is a GOAL node, then move the node to the Closed list and exit the loop returning ‘True’.
The solution can be found by backtracking the path
• If N is not the GOAL node, expand node N to generate the ‘immediate’ next nodes linked to
node N and add all those to the OPEN list
• Reorder the nodes in the OPEN list in ascending order according to an evaluation function f(n)
4/4/2023 25
27. ADVANTAGES AND DISADVANTAGES OF
BEST FIRST SEARCH
• Advantages:
1. Can switch between BFS and DFS, thus gaining the advantages of
both.
2. More efficient when compared to DFS.
• Disadvantages:
1. Chances of getting stuck in a loop are higher.
4/4/2023 27
28. A* SEARCH ALGORITHM
A* search is the most commonly known form of best-first search. It uses heuristic function h(n),
and cost to reach the node n from the start state g(n). It has combined features of UCS and
greedy best-first search, by which it solve the problem efficiently.
A* search algorithm finds the shortest path through the search space using the heuristic
function. This search algorithm expands less search tree and provides optimal result faster. A*
algorithm is similar to UCS except that it uses g(n)+h(n) instead of g(n).
In A* search algorithm, we use search heuristic as well as the cost to reach the node. Hence we
can combine both costs as following, and this sum is called as a fitness number.
Example
4/4/2023 28
29. ALGORITHM
Step1: Place the starting node in the OPEN list.
Step 2: Check if the OPEN list is empty or not, if the list is empty then return failure and
stops.
Step 3: Select the node from the OPEN list which has the smallest value of evaluation
function (g+h), if node n is goal node then return success and stop, otherwise
Step 4: Expand node n and generate all of its successors, and put n into the closed list.
For each successor n', check whether n' is already in the OPEN or CLOSED list, if not
then compute evaluation function for n' and place into Open list.
Step 5: Else if node n' is already in OPEN and CLOSED, then it should be attached to the
back pointer which reflects the lowest g(n') value.
Step 6: Return to Step 2.
4/4/2023 29
30. Advantages:
• A* search algorithm is the best algorithm than other search algorithms.
• A* search algorithm is optimal and complete.
• This algorithm can solve very complex problems.
Disadvantages:
• It does not always produce the shortest path as it mostly based on heuristics and
approximation.
• A* search algorithm has some complexity issues.
• The main drawback of A* is memory requirement as it keeps all generated nodes in the
memory, so it is not practical for various large-scale problems.
4/4/2023 30