Information Technology ACT, 2000 by Bharat Sir Kokar

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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।

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बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
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NEET
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IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
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for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
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for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
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for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट
Buddha Science & Technical Institute
Kokar, Ranchi, Jharkhand
An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th
Seminar on “The Information Technology ACT, 2000”
Join
for
NEET
Join
for
IIT-JEE

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Join Buddha Science and Technical Institute , Kokar for Strong Foundation IIT-JEE / Medical with 11th / 12th. For more details visit www.bharatsir.com and contact Bharat Bhushan @ B. K. NAL ( Assistant Professor), Computer Science & Director, Buddha Science and Technical Institute, Chunna Bhatta, Kokar, Ranchi-834001, Jharkhand, India. Mobile No : 0091-9835376044 WhatsApp No : 0091-9006365889. Excellent environment for IIT-JEE/NEET Preparation at Ranchi.

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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*.

Heuristics Search Techniques in AI

Heuristics Search Techniques in AI

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*.

Heuristics Search Techniques

Heuristics Search Techniques

Heuristics Search Techniques

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.

Hill Climbing Algorithm in Artificial Intelligence

Hill Climbing Algorithm in Artificial Intelligence

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.

Hill Climbing Algorithm in Artificial Intelligence

Hill Climbing Algorithm in Artificial Intelligence

Hill Climbing Algorithm in 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. 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.

Control Strategies in AI

Control Strategies in AI

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.

Control Strategies in AI

Control Strategies in AI

Control Strategies in AI

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.

Dynamic Programming

Dynamic Programming

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

Algorithm Design Technique

Algorithm Design Technique

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.

Production System in AI

Production System in AI

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.

Production System in AI

Production System 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.

Problem Characteristics in Artificial Intelligence

Problem Characteristics in Artificial Intelligence

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

Problem Characteristics in Artificial Intelligence

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

Introduction to Artificial Intteligence

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

Introduction to Artificial Intteligence

Introduction to Artificial Intteligence

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 by Bharat Sir

DESIGN AND ANALYSIS OF ALGORITHMS by Bharat Sir

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

DESIGN AND ANALYSIS OF ALGORITHMS

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

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 SYLLABUS

ARTIFICIAL INTELLIGENCE SYLLABUS

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NIMCET 2023 : Questions

NIMCET 2023 : Questions

NIMCET 2023 : Questions

Heuristics Search Techniques in AI

Heuristics Search Techniques in AI

Heuristics Search Techniques in AI

Heuristics Search Techniques

Heuristics Search Techniques

Heuristics Search Techniques

Hill Climbing Algorithm in Artificial Intelligence

Hill Climbing Algorithm in Artificial Intelligence

Hill Climbing Algorithm in Artificial Intelligence

Hill Climbing Algorithm in Artificial Intelligence

Hill Climbing Algorithm in Artificial Intelligence

Hill Climbing Algorithm in Artificial Intelligence

Control Strategies in AI

Control Strategies in AI

Control Strategies in AI

Control Strategies in AI

Control Strategies in AI

Control Strategies in AI

Dynamic Programming

Dynamic Programming

Dynamic Programming

Algorithm Design Technique

Algorithm Design Technique

Algorithm Design Technique

Production System in AI

Production System in AI

Production System in AI

Production System in AI

Production System in AI

Production System in AI

Problem Characteristics in Artificial Intelligence

Problem Characteristics in Artificial Intelligence

Problem Characteristics in Artificial Intelligence

Problem Characteristics in Artificial Intelligence

Problem Characteristics in Artificial Intelligence

Problem Characteristics in Artificial Intelligence

Introduction to Artificial Intteligence

Introduction to Artificial Intteligence

Introduction to Artificial Intteligence

Introduction to Artificial Intteligence

Introduction to Artificial Intteligence

Introduction to Artificial Intteligence

DESIGN AND ANALYSIS OF ALGORITHMS by Bharat Sir

DESIGN AND ANALYSIS OF ALGORITHMS by Bharat Sir

DESIGN AND ANALYSIS OF ALGORITHMS by Bharat Sir

DESIGN AND ANALYSIS OF ALGORITHMS

DESIGN AND ANALYSIS OF ALGORITHMS

DESIGN AND ANALYSIS OF ALGORITHMS

ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

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ARTIFICIAL INTELLIGENCE SYLLABUS

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1. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

2. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

3. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

4. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

5. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

6. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

7. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

8. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

9. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

10. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

11. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

12. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

13. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

14. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

15. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

16. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

17. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

18. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

19. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE

20. बुद्धा साइन्स एंड टेक्निकल इंस्टीट्यूट Buddha Science & Technical Institute Kokar, Ranchi, Jharkhand An Institution for Strong Foundation IIT-JEE / NEET With 11th & 12th Seminar on “The Information Technology ACT, 2000” Join for NEET Join for IIT-JEE