ARTIFICIAL INTELLIGENCE SYLLABUS BCA (1).pdf
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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.
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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
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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*.
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*.
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
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.
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.
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.
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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
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.
Similar to ARTIFICIAL INTELLIGENCE SYLLABUS BCA (1).pdf (13)
Problem Characteristics in Artificial Intelligence
Problem Characteristics in Artificial Intelligence
Problem Characteristics in Artificial Intelligence
Problem Characteristics in Artificial Intelligence
More from Bharat Bhushan @ B K NAL
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.
Dhruba Mitra December 2021
प्रकाशक एवं स्वामी -भारत भूषण । मुद्रक-ध्रुब मित्र । सरस्वती प्रेस प्रोडक्टस् इन्डस्ट्रीयल एरिया, कोकर, राँची - 834001 (झाररवण्ड) से मुद्रित। प्रकाषन स्थल - चुना भट्ठा, कोकर, राँची - 834001 (झाररवण्ड) । सम्पादक - ध्रुब मित्र।
Polish Notation in Data Structure by Bharat Sir, Kokar
Training Program on " Polish Notation in Data Structure" organised by Buddha Science & Technical Institute(BSTI), Ranchi In association with IRDA. Date 09 January 2022 at 8:00 PM to 9:00 PM(IST) on Google meet platform.
#BKNAL #BSTI #datastructure #BharatSir #polish_notation
Live Training Program on Linked List with Bharat Sir at Kokar
Ranchi 02,January : बुद्धा साइंस एंड टेक्निकल इंस्टिट्यूट के द्वारा “ लिंक्ड लिस्ट इन डाटा स्ट्रक्चर” कार्यक्रम के दौरान छात्र छात्राओं को लाइव प्रशिक्षण दिया गया। कार्यक्रम के आयोजन में इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडेजी ने सहयोग किया एवं छात्रों को संबोधित करते हुए कहा कि ऐसे लाइव प्रशिक्षण से विषय समझने में आसानी होगी। इंफॉर्मेशन साइंटिस्ट, भारत भूषण ने छात्रों को लिंक्ड लिस्ट के बारे में विस्तृत जानकारी दिए एवं लिंक्ड लिस्ट में प्रयोग होने वाले ऑपरेशन को लाइव कोडिंग कर दिखाया।
Live Training Program on Linked List with Bharat Sir at Kokar
Ranchi 02,January : बुद्धा साइंस एंड टेक्निकल इंस्टिट्यूट के द्वारा “ लिंक्ड लिस्ट इन डाटा स्ट्रक्चर” कार्यक्रम के दौरान छात्र छात्राओं को लाइव प्रशिक्षण दिया गया। कार्यक्रम के आयोजन में इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडेजी ने सहयोग किया एवं छात्रों को संबोधित करते हुए कहा कि ऐसे लाइव प्रशिक्षण से विषय समझने में आसानी होगी। इंफॉर्मेशन साइंटिस्ट, भारत भूषण ने छात्रों को लिंक्ड लिस्ट के बारे में विस्तृत जानकारी दिए एवं लिंक्ड लिस्ट में प्रयोग होने वाले ऑपरेशन को लाइव कोडिंग कर दिखाया।
Live Training Program on Linked List with Bharat Sir at Kokar
Ranchi 02,January : बुद्धा साइंस एंड टेक्निकल इंस्टिट्यूट के द्वारा “ लिंक्ड लिस्ट इन डाटा स्ट्रक्चर” कार्यक्रम के दौरान छात्र छात्राओं को लाइव प्रशिक्षण दिया गया। कार्यक्रम के आयोजन में इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडेजी ने सहयोग किया एवं छात्रों को संबोधित करते हुए कहा कि ऐसे लाइव प्रशिक्षण से विषय समझने में आसानी होगी। इंफॉर्मेशन साइंटिस्ट, भारत भूषण ने छात्रों को लिंक्ड लिस्ट के बारे में विस्तृत जानकारी दिए एवं लिंक्ड लिस्ट में प्रयोग होने वाले ऑपरेशन को लाइव कोडिंग कर दिखाया।
Live Training Program on Linked List with Bharat Sir at Kokar
Ranchi 02,January : बुद्धा साइंस एंड टेक्निकल इंस्टिट्यूट के द्वारा “ लिंक्ड लिस्ट इन डाटा स्ट्रक्चर” कार्यक्रम के दौरान छात्र छात्राओं को लाइव प्रशिक्षण दिया गया। कार्यक्रम के आयोजन में इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडेजी ने सहयोग किया एवं छात्रों को संबोधित करते हुए कहा कि ऐसे लाइव प्रशिक्षण से विषय समझने में आसानी होगी। इंफॉर्मेशन साइंटिस्ट, भारत भूषण ने छात्रों को लिंक्ड लिस्ट के बारे में विस्तृत जानकारी दिए एवं लिंक्ड लिस्ट में प्रयोग होने वाले ऑपरेशन को लाइव कोडिंग कर दिखाया।
Live Training Program on Linked List with Bharat Sir
Ranchi 02,January : बुद्धा साइंस एंड टेक्निकल इंस्टिट्यूट के द्वारा “ लिंक्ड लिस्ट इन डाटा स्ट्रक्चर” कार्यक्रम के दौरान छात्र छात्राओं को लाइव प्रशिक्षण दिया गया। कार्यक्रम के आयोजन में इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडेजी ने सहयोग किया एवं छात्रों को संबोधित करते हुए कहा कि ऐसे लाइव प्रशिक्षण से विषय समझने में आसानी होगी। इंफॉर्मेशन साइंटिस्ट, भारत भूषण ने छात्रों को लिंक्ड लिस्ट के बारे में विस्तृत जानकारी दिए एवं लिंक्ड लिस्ट में प्रयोग होने वाले ऑपरेशन को लाइव कोडिंग कर दिखाया।
Training Program on Linked List with Bharat Sir
Ranchi 02,January : बुद्धा साइंस एंड टेक्निकल इंस्टिट्यूट के द्वारा “ लिंक्ड लिस्ट इन डाटा स्ट्रक्चर” कार्यक्रम के दौरान छात्र छात्राओं को लाइव प्रशिक्षण दिया गया। कार्यक्रम के आयोजन में इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडेजी ने सहयोग किया एवं छात्रों को संबोधित करते हुए कहा कि ऐसे लाइव प्रशिक्षण से विषय समझने में आसानी होगी। इंफॉर्मेशन साइंटिस्ट, भारत भूषण ने छात्रों को लिंक्ड लिस्ट के बारे में विस्तृत जानकारी दिए एवं लिंक्ड लिस्ट में प्रयोग होने वाले ऑपरेशन को लाइव कोडिंग कर दिखाया।
Data Structure : Linked List with Bharat Sir
Ranchi 02,January : बुद्धा साइंस एंड टेक्निकल इंस्टिट्यूट के द्वारा “ लिंक्ड लिस्ट इन डाटा स्ट्रक्चर” कार्यक्रम के दौरान छात्र छात्राओं को लाइव प्रशिक्षण दिया गया। कार्यक्रम के आयोजन में इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडेजी ने सहयोग किया एवं छात्रों को संबोधित करते हुए कहा कि ऐसे लाइव प्रशिक्षण से विषय समझने में आसानी होगी। इंफॉर्मेशन साइंटिस्ट, भारत भूषण ने छात्रों को लिंक्ड लिस्ट के बारे में विस्तृत जानकारी दिए एवं लिंक्ड लिस्ट में प्रयोग होने वाले ऑपरेशन को लाइव कोडिंग कर दिखाया।
Data Structure : Linked List with Bharat Sir
Ranchi 02,January : बुद्धा साइंस एंड टेक्निकल इंस्टिट्यूट के द्वारा “ लिंक्ड लिस्ट इन डाटा स्ट्रक्चर” कार्यक्रम के दौरान छात्र छात्राओं को लाइव प्रशिक्षण दिया गया। कार्यक्रम के आयोजन में इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडेजी ने सहयोग किया एवं छात्रों को संबोधित करते हुए कहा कि ऐसे लाइव प्रशिक्षण से विषय समझने में आसानी होगी। इंफॉर्मेशन साइंटिस्ट, भारत भूषण ने छात्रों को लिंक्ड लिस्ट के बारे में विस्तृत जानकारी दिए एवं लिंक्ड लिस्ट में प्रयोग होने वाले ऑपरेशन को लाइव कोडिंग कर दिखाया।
Data Structure : Linked List
Ranchi 02,January : बुद्धा साइंस एंड टेक्निकल इंस्टिट्यूट के द्वारा “ लिंक्ड लिस्ट इन डाटा स्ट्रक्चर” कार्यक्रम के दौरान छात्र छात्राओं को लाइव प्रशिक्षण दिया गया। कार्यक्रम के आयोजन में इंटरनेशनल रिसर्च एंड डेवलपमेंट एसोसिएशन के वाइस प्रेसिडेंट शशि भूषण पांडेजी ने सहयोग किया एवं छात्रों को संबोधित करते हुए कहा कि ऐसे लाइव प्रशिक्षण से विषय समझने में आसानी होगी। इंफॉर्मेशन साइंटिस्ट, भारत भूषण ने छात्रों को लिंक्ड लिस्ट के बारे में विस्तृत जानकारी दिए एवं लिंक्ड लिस्ट में प्रयोग होने वाले ऑपरेशन को लाइव कोडिंग कर दिखाया।
Live Coding for Students : Linked List in Data Structure
Training Program on " Live Coding for Students : Linked List in Data Structure" organised by Buddha Science & Technical Institute(BSTI), Ranchi. Date 02 January 2022 at 8:00 PM to 9:00 PM(IST) on Google meet platform.
#BKNAL #BSTI #datastructure #BharatSir #Linkedlist
Dhruba Mitra News Paper
प्रकाषक एवं स्वामी -भारत भूषण । मुद्रक-ध्रुब मि़त्र। । सरस्वती प्रेस प्रोडक्टस् इन्डस्ट्रीयल एरिया , कोकर , राँची - 834001 (झाररवण्ड) से मुद्रित।
प्रकाषन स्थल - चुना भट्ठा , कोकर , राँची - 834001 (झाररवण्ड) ।
सम्पादक - ध्रुब मि़त्र। (मो0 - 09835376044)
Dhruba Mitra News Paper
प्रकाषक एवं स्वामी -भारत भूषण । मुद्रक-ध्रुब मि़त्र। । सरस्वती प्रेस प्रोडक्टस् इन्डस्ट्रीयल एरिया , कोकर , राँची - 834001 (झाररवण्ड) से मुद्रित।
प्रकाषन स्थल - चुना भट्ठा , कोकर , राँची - 834001 (झाररवण्ड) ।
सम्पादक - ध्रुब मि़त्र। (मो0 - 09835376044)
JAC Admission For Math Physics : Bharat Sir , Kokar
Buddha Science & Technical Institute is an Educational Wing of Lord Gautam Buddha Trust . It is situated at Chunna Bhatta , Kokar, Ranchi near Birsa Samadhi Asthal, Jharkhand. Their Mobile number is 09835376044 & 09199847964 WhatsApp Number 09006365889 and website is www.bharatsir.com. Founder of this Institution is Information Scientist Bharat Bhushan. The main objective of this institution is to provide a better environment for preparation of IIT-JEE & Medical. BSTI Provides Excellent educational facilities at low cost for economically poor students. BSTI forms Strong foundation for XI th & XIIth Class’s students with IIT-JEE & Medical. It Also Provides technical education in the field of Computer Science like C , C++ , JAVA, Python and other Programming Languages. BSTI runs free Computer Classes for Poor Students. Many Students have qualified in Engineering and Medical Examinations. You can Join the class for Strong Foundation of XIth , XII th with IIT-JEE & Medical Preparation.
Contact Details
Mobile : 9835376044 / 9006365889 / 9199847964
Durga Mandir Lane , Chunna Bhatta , Kokar ,Ranchi-834001,Jharkhand
Website : www.bharatsir.com
JAC Admission For Math Physics : Bharat Sir , Kokar
Buddha Science & Technical Institute is an Educational Wing of Lord Gautam Buddha Trust . It is situated at Chunna Bhatta , Kokar, Ranchi near Birsa Samadhi Asthal, Jharkhand. Their Mobile number is 09835376044 & 09199847964 WhatsApp Number 09006365889 and website is www.bharatsir.com. Founder of this Institution is Information Scientist Bharat Bhushan. The main objective of this institution is to provide a better environment for preparation of IIT-JEE & Medical. BSTI Provides Excellent educational facilities at low cost for economically poor students. BSTI forms Strong foundation for XI th & XIIth Class’s students with IIT-JEE & Medical. It Also Provides technical education in the field of Computer Science like C , C++ , JAVA, Python and other Programming Languages. BSTI runs free Computer Classes for Poor Students. Many Students have qualified in Engineering and Medical Examinations. You can Join the class for Strong Foundation of XIth , XII th with IIT-JEE & Medical Preparation.
Contact Details
Mobile : 9835376044 / 9006365889 / 9199847964
Durga Mandir Lane , Chunna Bhatta , Kokar ,Ranchi-834001,Jharkhand
Website : www.bharatsir.com
JAC Admission For Math Physics : Bharat Sir , Kokar
Buddha Science & Technical Institute is an Educational Wing of Lord Gautam Buddha Trust . It is situated at Chunna Bhatta , Kokar, Ranchi near Birsa Samadhi Asthal, Jharkhand. Their Mobile number is 09835376044 & 09199847964 WhatsApp Number 09006365889 and website is www.bharatsir.com. Founder of this Institution is Information Scientist Bharat Bhushan. The main objective of this institution is to provide a better environment for preparation of IIT-JEE & Medical. BSTI Provides Excellent educational facilities at low cost for economically poor students. BSTI forms Strong foundation for XI th & XIIth Class’s students with IIT-JEE & Medical. It Also Provides technical education in the field of Computer Science like C , C++ , JAVA, Python and other Programming Languages. BSTI runs free Computer Classes for Poor Students. Many Students have qualified in Engineering and Medical Examinations. You can Join the class for Strong Foundation of XIth , XII th with IIT-JEE & Medical Preparation.
Contact Details
Mobile : 9835376044 / 9006365889 / 9199847964
Durga Mandir Lane , Chunna Bhatta , Kokar ,Ranchi-834001,Jharkhand
Website : www.bharatsir.com
Chunna Bhatta, Kokar, Ranchi
Buddha Science & Technical Institute is an Educational Wing of Lord Gautam Buddha Trust . It is situated at Chunna Bhatta , Kokar, Ranchi near Birsa Samadhi Asthal, Jharkhand. Their Mobile number is 09835376044 & 09199847964 WhatsApp Number 09006365889 and website is www.bharatsir.com. Founder of this Institution is Information Scientist Bharat Bhushan. The main objective of this institution is to provide a better environment for preparation of IIT-JEE & Medical. BSTI Provides Excellent educational facilities at low cost for economically poor students. BSTI forms Strong foundation for XI th & XIIth Class’s students with IIT-JEE & Medical. It Also Provides technical education in the field of Computer Science like C , C++ , JAVA, Python and other Programming Languages. BSTI runs free Computer Classes for Poor Students. Many Students have qualified in Engineering and Medical Examinations. You can Join the class for Strong Foundation of XIth , XII th with IIT-JEE & Medical Preparation.
Chunna Bhatta, Kokar, Ranchi
Buddha Science & Technical Institute is an Educational Wing of Lord Gautam Buddha Trust . It is situated at Chunna Bhatta , Kokar, Ranchi near Birsa Samadhi Asthal, Jharkhand. Their Mobile number is 09835376044 & 09199847964 WhatsApp Number 09006365889 and website is www.bharatsir.com. Founder of this Institution is Information Scientist Bharat Bhushan. The main objective of this institution is to provide a better environment for preparation of IIT-JEE & Medical. BSTI Provides Excellent educational facilities at low cost for economically poor students. BSTI forms Strong foundation for XI th & XIIth Class’s students with IIT-JEE & Medical. It Also Provides technical education in the field of Computer Science like C , C++ , JAVA, Python and other Programming Languages. BSTI runs free Computer Classes for Poor Students. Many Students have qualified in Engineering and Medical Examinations. You can Join the class for Strong Foundation of XIth , XII th with IIT-JEE & Medical Preparation.
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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 ARTIFICIAL INTELLIGENCE
- 2. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence UNIT-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.
- 3. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence UNIT- 2 Problem Solving and Searching Techniques Problem Characteristics Production Systems Control Strategies, Breadth First Search Depth First Search Hill climbing and its Variations
- 4. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence 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.
- 5. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence UNIT- 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)
- 6. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence UNIT- 4 Dealing with Uncertainty and Inconsistencies Maintenance System Default Reasoning Probabilistic Reasoning Bayesian Probabilistic Inference Possible World Representations.
- 7. www.bharatsir.com || Mo No : 09835376044 || WhtasApp : 09006365889 Artificial Intelligence UNIT- 5 Dealing with Uncertainty and Inconsistencies Parsing Techniques Context-Free and Transformational Grammars Recursive and Augmented Transition Nets.
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