Control Strategies in AI
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Control Strategies Control Strategy in Artificial Intelligence scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space. It helps us to decide which rule has to apply next without getting stuck at any point. Characteristics of Control Strategies A good Control strategy has two main characteristics: Control Strategy should cause Motion Control strategy should be Systematic Co ntrol Strategy should cause Motion Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem. Co ntrol Strategy should be Systematic Though the strategy applied should create the motion but if do not follow some systematic strategy than we are likely to reach the same state number of times before reaching the solution which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
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Control Strategies in AI
Control Strategies
Control Strategy in Artificial Intelligence
scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space.
It helps us to decide which rule has to apply next without getting stuck at any point.
Characteristics of Control Strategies
A good Control strategy has two main
characteristics:
Control Strategy should cause Motion
Control strategy should be Systematic
Co ntrol Strategy should cause Motion
Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
Co ntrol Strategy should be Systematic
Though the strategy applied should create the
motion but if do not follow some systematic
strategy than we are likely to reach the same state
number of times before reaching the solution
which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
Production System in AI
Production System in Artificial Intelligence (AI)
A production system in AI helps create AI-based computer programs. With the help of it, the automation of various types of machines has become an easy task. The types of machines can be a computer, mobile applications, manufacturing tools, or more. The set of rules in a production system in Artificial Intelligence defines the behavior of the machine. It helps the machine respond to the surroundings.
A production system in AI is a type of cognitive architecture that defines specific actions as per certain rules. The rules represent the declarative knowledge of a machine to respond according to different conditions. Today, many expert systems and automation methodologies rely on the rules of production systems.
Global Database
A global database consists of the architecture used as a central data structure. A database contains all the necessary data and information required for the successful completion of a task. It can be divided into two parts as permanent and temporary. The permanent part of the database consists of fixed actions, whereas the temporary part alters according to circumstances.
Learn more about Artificial Neural networks in this insightful Artificial Intelligence Training now!
Production Rules
Production rules in AI are the set of rules that operates on the data fetched from the global database. Also, these production rules are bound with precondition and postcondition that gets checked by the database. If a condition is passed through a production rule and gets satisfied by the global database, then the rule is successfully applied. The rules are of the form A®B, where the right-hand side represents an outcome corresponding to the problem state represented by the left-hand side.
Control System
The control system checks the applicability of a rule. It helps decide which rule should be applied and terminates the process when the system gives the correct output. It also resolves the conflict of multiple conditions arriving at the same time. The strategy of the control system specifies the sequence of rules that compares the condition from the global database to reach the correct result.
Simplicity
The production rule in AI is in the form of an ‘IF-THEN’ statement. Every rule in the production system has a unique structure. It helps represent knowledge and reasoning in the simplest way possible to solve real-world problems. Also, it helps improve the readability and understanding of the production rules.
Hill Climbing Algorithm in Artificial Intelligence
Hill Climbing Algorithm in Artificial Intelligence
Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value.
Hill climbing algorithm is a technique which is used for optimizing the mathematical problems. One of the widely discussed examples of Hill climbing algorithm is Traveling-salesman Problem in which we need to minimize the distance traveled by the salesman.
It is also called greedy local search as it only looks to its good immediate neighbor state and not beyond that.
A node of hill climbing algorithm has two components which are state and value.
Hill Climbing is mostly used when a good heuristic is available.
In this algorithm, we don't need to maintain and handle the search tree or graph as it only keeps a single current state.
Features of Hill Climbing:
Following are some main features of Hill Climbing Algorithm:
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.
State-space Diagram for Hill Climbing:
The state-space landscape is a graphical representation of the hill-climbing algorithm which is showing a graph between various states of algorithm and Objective function/Cost.
On Y-axis we have taken the function which can be an objective function or cost function, and state-space on the x-axis. If the function on Y-axis is cost then, the goal of search is to find the global minimum and local minimum. If the function of Y-axis is Objective function, then the goal of the search is to find the global maximum and local maximum.
Artificial Intelligence Notes Unit 1
Artificial Intelligence Unit 1 for CSE 8th Semester (A/c to CSVTU, Bhilai Syllabus)
Recommended
Control Strategies in AI
Control Strategies
Control Strategy in Artificial Intelligence
scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space.
It helps us to decide which rule has to apply next without getting stuck at any point.
Characteristics of Control Strategies
A good Control strategy has two main
characteristics:
Control Strategy should cause Motion
Control strategy should be Systematic
Co ntrol Strategy should cause Motion
Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
Co ntrol Strategy should be Systematic
Though the strategy applied should create the
motion but if do not follow some systematic
strategy than we are likely to reach the same state
number of times before reaching the solution
which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
Production System in AI
Production System in Artificial Intelligence (AI)
A production system in AI helps create AI-based computer programs. With the help of it, the automation of various types of machines has become an easy task. The types of machines can be a computer, mobile applications, manufacturing tools, or more. The set of rules in a production system in Artificial Intelligence defines the behavior of the machine. It helps the machine respond to the surroundings.
A production system in AI is a type of cognitive architecture that defines specific actions as per certain rules. The rules represent the declarative knowledge of a machine to respond according to different conditions. Today, many expert systems and automation methodologies rely on the rules of production systems.
Global Database
A global database consists of the architecture used as a central data structure. A database contains all the necessary data and information required for the successful completion of a task. It can be divided into two parts as permanent and temporary. The permanent part of the database consists of fixed actions, whereas the temporary part alters according to circumstances.
Learn more about Artificial Neural networks in this insightful Artificial Intelligence Training now!
Production Rules
Production rules in AI are the set of rules that operates on the data fetched from the global database. Also, these production rules are bound with precondition and postcondition that gets checked by the database. If a condition is passed through a production rule and gets satisfied by the global database, then the rule is successfully applied. The rules are of the form A®B, where the right-hand side represents an outcome corresponding to the problem state represented by the left-hand side.
Control System
The control system checks the applicability of a rule. It helps decide which rule should be applied and terminates the process when the system gives the correct output. It also resolves the conflict of multiple conditions arriving at the same time. The strategy of the control system specifies the sequence of rules that compares the condition from the global database to reach the correct result.
Simplicity
The production rule in AI is in the form of an ‘IF-THEN’ statement. Every rule in the production system has a unique structure. It helps represent knowledge and reasoning in the simplest way possible to solve real-world problems. Also, it helps improve the readability and understanding of the production rules.
Hill Climbing Algorithm in Artificial Intelligence
Hill Climbing Algorithm in Artificial Intelligence
Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value.
Hill climbing algorithm is a technique which is used for optimizing the mathematical problems. One of the widely discussed examples of Hill climbing algorithm is Traveling-salesman Problem in which we need to minimize the distance traveled by the salesman.
It is also called greedy local search as it only looks to its good immediate neighbor state and not beyond that.
A node of hill climbing algorithm has two components which are state and value.
Hill Climbing is mostly used when a good heuristic is available.
In this algorithm, we don't need to maintain and handle the search tree or graph as it only keeps a single current state.
Features of Hill Climbing:
Following are some main features of Hill Climbing Algorithm:
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.
State-space Diagram for Hill Climbing:
The state-space landscape is a graphical representation of the hill-climbing algorithm which is showing a graph between various states of algorithm and Objective function/Cost.
On Y-axis we have taken the function which can be an objective function or cost function, and state-space on the x-axis. If the function on Y-axis is cost then, the goal of search is to find the global minimum and local minimum. If the function of Y-axis is Objective function, then the goal of the search is to find the global maximum and local maximum.
Artificial Intelligence Notes Unit 1
Artificial Intelligence Unit 1 for CSE 8th Semester (A/c to CSVTU, Bhilai Syllabus)
State Space Search and Control Strategies in Artificial Intelligence.pptx
In here, I gave PowerPoint Presentation on State Space Search and Control Strategies in Artificial Intelligence.
For More Videos Please Like Share Subscribe to my Youtube Channel
https://www.youtube.com/@learnaiwithshankar
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AI Lecture 4 (informed search and exploration)
cse 412 - artificial intelligence, Lectures by Tajim Md. Niamat Ullah Akhund.
Heuristics Search Techniques
What is Heuristics?
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.
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.
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.
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.
Heuristic techniques
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.
We can perform the Heuristic techniques into two categories:
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*.
Problem Solving
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.
Artificial Intelligence - Game of Nim
Presentation on how we made a game where the computer gets smarter the more times it plays a user.
Heuristics Search Techniques in AI
What is Heuristics?
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.
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.
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.
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.
Heuristic techniques
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.
We can perform the Heuristic techniques into two categories:
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*.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
Heuristic Search Techniques {Artificial Intelligence}
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.”
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Artificial Intelligence: What Is Reinforcement Learning?
Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses. In this SlideShare, I want to provide a simple guide that explains reinforcement learning and give you some practical examples of how it is used today.
Heuristic Search Techniques Unit -II.ppt
AI, Heuristic algorithms, generate and test,
Hill climbing Algorithm
A* algorithm
I. Hill climbing algorithm II. Steepest hill climbing 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
Production System in AI
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Artificial Intelligence
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
More Related Content
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State Space Search and Control Strategies in Artificial Intelligence.pptx
In here, I gave PowerPoint Presentation on State Space Search and Control Strategies in Artificial Intelligence.
For More Videos Please Like Share Subscribe to my Youtube Channel
https://www.youtube.com/@learnaiwithshankar
For More PowerPoint Presentations, Please Follow Me.
https://www.slideshare.net/RSAISHANKAR?from_search=0
Thank you
AI Lecture 4 (informed search and exploration)
cse 412 - artificial intelligence, Lectures by Tajim Md. Niamat Ullah Akhund.
Heuristics Search Techniques
What is Heuristics?
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.
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.
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.
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.
Heuristic techniques
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.
We can perform the Heuristic techniques into two categories:
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*.
Problem Solving
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.
Artificial Intelligence - Game of Nim
Presentation on how we made a game where the computer gets smarter the more times it plays a user.
Heuristics Search Techniques in AI
What is Heuristics?
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.
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.
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.
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.
Heuristic techniques
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.
We can perform the Heuristic techniques into two categories:
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*.
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
Heuristic Search Techniques {Artificial Intelligence}
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
Artificial Intelligence: What Is Reinforcement Learning?
Reinforcement learning is one of the most discussed, followed and contemplated topics in artificial intelligence (AI) as it has the potential to transform most businesses. In this SlideShare, I want to provide a simple guide that explains reinforcement learning and give you some practical examples of how it is used today.
Heuristic Search Techniques Unit -II.ppt
AI, Heuristic algorithms, generate and test,
Hill climbing Algorithm
A* algorithm
I. Hill climbing algorithm II. Steepest hill climbing 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
What's hot (20)
State Space Search and Control Strategies in Artificial Intelligence.pptx
State Space Search and Control Strategies in Artificial Intelligence.pptx
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1
Heuristic Search Techniques {Artificial Intelligence}
Heuristic Search Techniques {Artificial Intelligence}
Artificial Intelligence: What Is Reinforcement Learning?
Artificial Intelligence: What Is Reinforcement Learning?
I. Hill climbing algorithm II. Steepest hill climbing algorithm
I. Hill climbing algorithm II. Steepest hill climbing algorithm
Similar to Control Strategies in AI
Production System in AI
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Artificial Intelligence
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Problem Characteristics in Artificial Intelligence
Artificial Intelligence is a “way of making a computer, a computer-controlled robot, or software think intelligently, in the similar manner the intelligent humans think”.
Since artificial intelligence (AI) is mainly related to the search process, it is important to have some methodology to choose the best possible solution.
To choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Problem Characteristics in Artificial Intelligence
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
ARTIFICIAL INTELLIGENCE
ARTIFICIAL INTELLIGENCE
1. Introduction
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
ARTIFICIAL INTELLIGENCE SYLLABUS BCA (1).pdf
ARTIFICIAL INTELLIGENCE
1. Introduction
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
ARTIFICIAL INTELLIGENCE
ARTIFICIAL INTELLIGENCE
1. Introduction
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
ARTIFICIAL INTELLIGENCE SYLLABUS
ARTIFICIAL INTELLIGENCE
1. Introduction
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
Introduction to Artificial Intteligence
ARTIFICIAL INTELLIGENCE : Introduction ,
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and
environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
Introduction to Artificial Intteligence
ARTIFICIAL INTELLIGENCE : Introduction ,
Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational
Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and
environment.
2. Problem Solving and Searching Techniques
Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First
Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A*
algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing,
Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation
Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets,
Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs.
Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies
Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic
Inference, Possible World Representations.
5. Understanding Natural Languages
Parsing Techniques, Context-Free and
Transformational Grammars, Recursive and Augmented Transition Nets.
Hill Climbing Algorithm in Artificial Intelligence
Hill Climbing Algorithm in Artificial Intelligence
Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value.
Hill climbing algorithm is a technique which is used for optimizing the mathematical problems. One of the widely discussed examples of Hill climbing algorithm is Traveling-salesman Problem in which we need to minimize the distance traveled by the salesman.
It is also called greedy local search as it only looks to its good immediate neighbor state and not beyond that.
A node of hill climbing algorithm has two components which are state and value.
Hill Climbing is mostly used when a good heuristic is available.
In this algorithm, we don't need to maintain and handle the search tree or graph as it only keeps a single current state.
Features of Hill Climbing:
Following are some main features of Hill Climbing Algorithm:
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.
State-space Diagram for Hill Climbing:
The state-space landscape is a graphical representation of the hill-climbing algorithm which is showing a graph between various states of algorithm and Objective function/Cost.
On Y-axis we have taken the function which can be an objective function or cost function, and state-space on the x-axis. If the function on Y-axis is cost then, the goal of search is to find the global minimum and local minimum. If the function of Y-axis is Objective function, then the goal of the search is to find the global maximum and local maximum.
The State of Applicant Tracking Systems 2020.pdf
An Applicant Tracking System (ATS) helps talent acquisition experts manage and organize the hiring process. This PDF comprises of the key findings of the survey conducted to understand the state of Applicant Tracking Systems in 2020. It also includes details about how prevalent applicant tracking systems are and the benefits of using an ATS. If you're looking for an applicant tracking and recruiting software, visit us at https://www.emptrust.com/ats/applicant-tracking-and-recruiting-software.aspx.
Machine learning and pattern recognition
In a very simple language, Pattern Recognition is a type of problem while Machine Learning is a type of solution. Pattern recognition is closely related to artificial intelligence and machine learning. Pattern Recognition is an engineering application of Machine Learning.
Machine Learning Presentation
"Unveiling the Magic of Machine Learning: Join me for a concise yet insightful presentation on the captivating world of Machine Learning (ML). Discover how ML algorithms transform data into predictive models, driving smarter decisions. From regression to classification and beyond, we'll delve into the basics, demystify key concepts, and showcase real-world applications. Let's explore the algorithms shaping our digital landscape and understand how they're revolutionizing industries. Don't miss this opportunity to grasp the essence of ML in a nutshell!"
Machine learning applications nurturing growth of various business domains
Machine learning is a science in which machines are becoming smarter and helping humans to make the best decisions based on previous data recommended practices. This technique is not new but is occupying fresh momentum. Machine Learning Algorithm learns from the previous records and analyses the data. Without any human interrupt, it will generate its own recommendation. A machine will add that recommendation as experience in its database and use it for further processing. In short, the machine learns from its own experience and gives you better and better output.
Machine learning is an iterative process as the more data added to machines learn from fresh feeds of data and then independently adapt new features to handle new data without constant human intervention. Machine learning was earlier used to predict what’s happing with the business but now the machine learning algorithm will suggest what action needs be taken by moving our business forward.
This PowerPoint presentation presents the results of a literature survey of machine learning applications nurturing the growth of various business domains. More specifically, it gives a brief introduction of Machine Learning, four major types of Machine Learning, enhancement in various business domains by the use of various machine learning algorithms.
Similar to Control Strategies in AI (20)
Problem Characteristics in Artificial Intelligence
Problem Characteristics in Artificial Intelligence
Problem Characteristics in Artificial Intelligence
Problem Characteristics in Artificial Intelligence
Hill Climbing Algorithm in Artificial Intelligence
Hill Climbing Algorithm in Artificial Intelligence
Machine learning applications nurturing growth of various business domains
Machine learning applications nurturing growth of various business domains
More from Bharat Bhushan
Dynamic Programming
Dynamic Programming :
Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems, solving each subproblem once, and storing the solution to each subproblem so that it can be reused in the future. Some characteristics of dynamic programming include:
Optimal substructure: Dynamic programming problems typically have an optimal substructure, meaning that the optimal solution to the problem can be obtained by solving the subproblems optimally and combining their solutions.
Overlapping subproblems: Dynamic programming problems often involve overlapping subproblems, meaning that the same subproblems are solved multiple times. To avoid solving the same subproblem multiple times, dynamic programming algorithms store the solutions to the subproblems in a table or array, so that they can be reused later.
Bottom-up approach: Dynamic programming algorithms usually solve problems using a bottom-up approach, meaning that they start by solving the smallest subproblems and work their way up to the larger ones.
Efficiency: Dynamic programming algorithms can be very efficient, especially when the subproblems overlap significantly. By storing the solutions to the subproblems and reusing them, dynamic programming algorithms can avoid redundant computations and achieve good time and space complexity.
Applicability: Dynamic programming is applicable to a wide range of problems, including optimization problems, decision problems, and problems that involve sequential decisions. It is often used to solve problems in computer science, operations research, and economics.
Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
Algorithm Design Technique
Dynamic Programming :
Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems, solving each subproblem once, and storing the solution to each subproblem so that it can be reused in the future. Some characteristics of dynamic programming include:
Optimal substructure: Dynamic programming problems typically have an optimal substructure, meaning that the optimal solution to the problem can be obtained by solving the subproblems optimally and combining their solutions.
Overlapping subproblems: Dynamic programming problems often involve overlapping subproblems, meaning that the same subproblems are solved multiple times. To avoid solving the same subproblem multiple times, dynamic programming algorithms store the solutions to the subproblems in a table or array, so that they can be reused later.
Bottom-up approach: Dynamic programming algorithms usually solve problems using a bottom-up approach, meaning that they start by solving the smallest subproblems and work their way up to the larger ones.
Efficiency: Dynamic programming algorithms can be very efficient, especially when the subproblems overlap significantly. By storing the solutions to the subproblems and reusing them, dynamic programming algorithms can avoid redundant computations and achieve good time and space complexity.
Applicability: Dynamic programming is applicable to a wide range of problems, including optimization problems, decision problems, and problems that involve sequential decisions. It is often used to solve problems in computer science, operations research, and economics.
Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
DESIGN AND ANALYSIS OF ALGORITHMS by Bharat Sir
DESIGN AND ANALYSIS OF ALGORITHMS
1. Introduction
Basic Design and Analysis techniques of Algorithms, Correctness of Algorithm.
2. Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
3. Sorting and Searching Techniques
Elementary sorting techniques–Bubble Sort, Insertion Sort, Merge Sort, Advanced Sorting
techniques - Heap Sort, Quick Sort, Sorting in Linear Time - Bucket Sort, Radix Sort and Count
Sort, Searching Techniques, Medians & Order Statistics, complexity analysis;
4. Lower Bounding Techniques
Decision Trees
5. Balanced Trees
Red-Black Trees
6. Advanced Analysis Technique
Amortized analysis
7. Graphs
Graph Algorithms–Breadth First Search, Depth First Search and its Applications, Minimum
Spanning Trees.
DESIGN AND ANALYSIS OF ALGORITHMS
DESIGN AND ANALYSIS OF ALGORITHMS
1. Introduction
Basic Design and Analysis techniques of Algorithms, Correctness of Algorithm.
2. Algorithm Design Techniques
Iterative techniques, Divide and Conquer, Dynamic Programming, Greedy Algorithms.
3. Sorting and Searching Techniques
Elementary sorting techniques–Bubble Sort, Insertion Sort, Merge Sort, Advanced Sorting
techniques - Heap Sort, Quick Sort, Sorting in Linear Time - Bucket Sort, Radix Sort and Count
Sort, Searching Techniques, Medians & Order Statistics, complexity analysis;
4. Lower Bounding Techniques
Decision Trees
5. Balanced Trees
Red-Black Trees
6. Advanced Analysis Technique
Amortized analysis
7. Graphs
Graph Algorithms–Breadth First Search, Depth First Search and its Applications, Minimum
Spanning Trees.
Cyber Security
Presentation on Cyber Security by Bharat Bhushan, Assistant Professor, Ram Lakhan Singh Yadav College, Kokar, Ranchi.
Cyber Security
Presentation on Cyber Security by Bharat Bhushan, Assistant Professor, Ram Lakhan Singh Yadav College, Kokar, Ranchi.
Digital Content Creation By Bharat Sir Kokar
रांची, 24 जुलाई : विश्व संवाद केंद्र झारखंड के तत्वाधान में डिजिटल कंटेंट क्रिएशन एंड यूटिलाइजेशन फॉर स्टूडेंट्स एंड इनफार्मेशन प्रोफेशनल विषय पर एक दिवसीय प्रशिक्षण कार्यक्रम का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, कोकर के सभागार में आयोजित किया गया। कार्यक्रम का उद्घाटन इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडे ने दीप प्रज्वलित कर किया ।
इंफॉर्मेशन साइंटिस्ट भारत भूषण ने छात्रों को डिजिटल कंटेंट क्रिएट करने और उसका उपयोग अपने और देश हित में कैसे कर सकते हैं इस विषय पर छात्रों को प्रशिक्षित किया।
कार्यक्रम के समापन में मुख्य अतिथि के तौर पर राम लखन सिंह यादव कॉलेज के प्रिंसिपल डॉ. जे.पी. सिंह ने छात्रों को कहा कि आज का दौर टेक्नोलॉजी और इंफॉर्मेशन का है। आज वही सबसे ज्यादा विकसित राष्ट्र कहलाएगा जिसके पास जितना ज्यादा से ज्यादा विकसित टेक्नोलॉजी होगा। कार्यक्रम के दौरान बेहतर प्रदर्शन करने वाले छात्रों को डॉ. जे.पी. सिंह ने मेडल देकर सम्मानित किया।
कार्यक्रम को सफल बनाने में सुप्रिया भारती, सावित्री कुमारी, हरेंद्र कुमार, निशांत दीप दास, छाया महतो, अंकित रंजन एवं किशोरी शाह का मुख्य योगदान रहा।
Digital Content Creation by Bharat Sir Kokar
रांची, 24 जुलाई : विश्व संवाद केंद्र झारखंड के तत्वाधान में डिजिटल कंटेंट क्रिएशन एंड यूटिलाइजेशन फॉर स्टूडेंट्स एंड इनफार्मेशन प्रोफेशनल विषय पर एक दिवसीय प्रशिक्षण कार्यक्रम का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, कोकर के सभागार में आयोजित किया गया। कार्यक्रम का उद्घाटन इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडे ने दीप प्रज्वलित कर किया ।
इंफॉर्मेशन साइंटिस्ट भारत भूषण ने छात्रों को डिजिटल कंटेंट क्रिएट करने और उसका उपयोग अपने और देश हित में कैसे कर सकते हैं इस विषय पर छात्रों को प्रशिक्षित किया।
कार्यक्रम के समापन में मुख्य अतिथि के तौर पर राम लखन सिंह यादव कॉलेज के प्रिंसिपल डॉ. जे.पी. सिंह ने छात्रों को कहा कि आज का दौर टेक्नोलॉजी और इंफॉर्मेशन का है। आज वही सबसे ज्यादा विकसित राष्ट्र कहलाएगा जिसके पास जितना ज्यादा से ज्यादा विकसित टेक्नोलॉजी होगा। कार्यक्रम के दौरान बेहतर प्रदर्शन करने वाले छात्रों को डॉ. जे.पी. सिंह ने मेडल देकर सम्मानित किया।
कार्यक्रम को सफल बनाने में सुप्रिया भारती, सावित्री कुमारी, हरेंद्र कुमार, निशांत दीप दास, छाया महतो, अंकित रंजन एवं किशोरी शाह का मुख्य योगदान रहा।
NEET PHYSICS RANCHI
10 जुलाई , 2022 : लॉर्ड गौतम बुद्धा ट्रस्ट एवं इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन, झारखंड के संयुक्त तत्वाधान में “ द इनफार्मेशन टेक्नोलॉजी एक्ट, 2000 ” पर सेमिनार का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, चूना भट्ठा, कोकर के सभागार में इंफॉर्मेशन साइंटिस्ट भारत भूषण के द्वारा दीप प्रज्वलित कर किया गया।
उन्होंने कहा कि आज के डिजिटल युग में गलती से भी गलती ना हो इसके लिए जरूरी है कि आम व्यक्ति भी सूचना तकनीक अधिनियम 2000 के बारे में जाने। इस दौर में हर के हाथ में मोबाइल है डिजिटल युग में गलती से भी गलती ना करें : इंफॉर्मेशन साइंटिस्ट भारतऔर हर मोबाइल इंटरनेट से कनेक्टेड है। टचस्क्रीन मोबाइल होने के कारण कभी-कभी फोटो या वीडियो कहीं भी गलती से शेयर हो जाता है जिसका कितना भयानक परिणाम हो सकता है आज कल देखने को मिल रहा है।
Bharat Sir Kokar
10 जुलाई , 2022 : लॉर्ड गौतम बुद्धा ट्रस्ट एवं इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन, झारखंड के संयुक्त तत्वाधान में “ द इनफार्मेशन टेक्नोलॉजी एक्ट, 2000 ” पर सेमिनार का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, चूना भट्ठा, कोकर के सभागार में इंफॉर्मेशन साइंटिस्ट भारत भूषण के द्वारा दीप प्रज्वलित कर किया गया।
उन्होंने कहा कि आज के डिजिटल युग में गलती से भी गलती ना हो इसके लिए जरूरी है कि आम व्यक्ति भी सूचना तकनीक अधिनियम 2000 के बारे में जाने। इस दौर में हर के हाथ में मोबाइल है डिजिटल युग में गलती से भी गलती ना करें : इंफॉर्मेशन साइंटिस्ट भारतऔर हर मोबाइल इंटरनेट से कनेक्टेड है। टचस्क्रीन मोबाइल होने के कारण कभी-कभी फोटो या वीडियो कहीं भी गलती से शेयर हो जाता है जिसका कितना भयानक परिणाम हो सकता है आज कल देखने को मिल रहा है।
Buddha Science and Technical Institute
10 जुलाई , 2022 : लॉर्ड गौतम बुद्धा ट्रस्ट एवं इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन, झारखंड के संयुक्त तत्वाधान में “ द इनफार्मेशन टेक्नोलॉजी एक्ट, 2000 ” पर सेमिनार का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, चूना भट्ठा, कोकर के सभागार में इंफॉर्मेशन साइंटिस्ट भारत भूषण के द्वारा दीप प्रज्वलित कर किया गया।
उन्होंने कहा कि आज के डिजिटल युग में गलती से भी गलती ना हो इसके लिए जरूरी है कि आम व्यक्ति भी सूचना तकनीक अधिनियम 2000 के बारे में जाने। इस दौर में हर के हाथ में मोबाइल है डिजिटल युग में गलती से भी गलती ना करें : इंफॉर्मेशन साइंटिस्ट भारतऔर हर मोबाइल इंटरनेट से कनेक्टेड है। टचस्क्रीन मोबाइल होने के कारण कभी-कभी फोटो या वीडियो कहीं भी गलती से शेयर हो जाता है जिसका कितना भयानक परिणाम हो सकता है आज कल देखने को मिल रहा है।
The Information Technology ACT, 2000
10 जुलाई , 2022 : लॉर्ड गौतम बुद्धा ट्रस्ट एवं इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन, झारखंड के संयुक्त तत्वाधान में “ द इनफार्मेशन टेक्नोलॉजी एक्ट, 2000 ” पर सेमिनार का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, चूना भट्ठा, कोकर के सभागार में इंफॉर्मेशन साइंटिस्ट भारत भूषण के द्वारा दीप प्रज्वलित कर किया गया।
NEET PHYSICS KOKAR
10 जुलाई , 2022 : लॉर्ड गौतम बुद्धा ट्रस्ट एवं इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन, झारखंड के संयुक्त तत्वाधान में “ द इनफार्मेशन टेक्नोलॉजी एक्ट, 2000 ” पर सेमिनार का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, चूना भट्ठा, कोकर के सभागार में इंफॉर्मेशन साइंटिस्ट भारत भूषण के द्वारा दीप प्रज्वलित कर किया गया।
Seminar on The Information Technology ACT
10 जुलाई , 2022 : लॉर्ड गौतम बुद्धा ट्रस्ट एवं इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन, झारखंड के संयुक्त तत्वाधान में “ द इनफार्मेशन टेक्नोलॉजी एक्ट, 2000 ” पर सेमिनार का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, चूना भट्ठा, कोकर के सभागार में इंफॉर्मेशन साइंटिस्ट भारत भूषण के द्वारा दीप प्रज्वलित कर किया गया।
Information Technology ACT, 2000 by Bharat Sir Kokar
10 जुलाई , 2022 : लॉर्ड गौतम बुद्धा ट्रस्ट एवं इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन, झारखंड के संयुक्त तत्वाधान में “ द इनफार्मेशन टेक्नोलॉजी एक्ट, 2000 ” पर सेमिनार का आयोजन बुद्धा साइंस एंड टेक्निकल इंस्टीट्यूट, चूना भट्ठा, कोकर के सभागार में इंफॉर्मेशन साइंटिस्ट भारत भूषण के द्वारा दीप प्रज्वलित कर किया गया।
NEET PHYSICS : BHARAT SIR
PHYSICS: NEET / IIT with Asst. Professor, Bharta Sir, Kokar. Join Buddha Science and Technical Institute (BSTI) at Chunna Bhatta, Kokar, Ranchi . Details: www.bharatsir.com | 9835376044/ 9006365889 #BharatSir #BKNAL #BSTI #Kokar #Bharatsir #NEET #JAC #PHYSICS #IITJEE #JAC
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- 1. Presentation on By BHARAT BHUSHAN Asst. Professor, Department of Computer Sc. RLSY College, Ranchi Buddha Science and Technical Institute, Ranchi www.bharatsir.com Control Strategies in Artificial Intelligence
- 2. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Control Strategies Control Strategy in Artificial Intelligence scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space. It helps us to decide which rule has to apply next without getting stuck at any point.
- 3. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Characteristics of Control Strategies A good Control strategy has two main characteristics: Control Strategy should cause Motion Control strategy should be Systematic
- 4. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Control Strategy should cause Motion Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
- 5. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Control Strategy should be Systematic Though the strategy applied should create the motion but if do not follow some systematic strategy than we are likely to reach the same state number of times before reaching the solution which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
- 6. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence Examples: Widely used Control Strategies are Breadth- First Search, Depth-First Search, Generate and Test, Hill-Climbing, Best-first search, Problem Reduction and many more.
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