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ARTIFICIAL INTELLIGENCE The thing to do is to make the best use of current situation and proceed. This is an example of an irrecoverable solution steps. 1. Ignorable problems Ex:- theorem proving. · In which solution steps can be ignored. 2. Recoverable problems Ex:- 8 puzzle. · In which solution steps can be undone. ARTIFICIAL INTELLIGENCE: QUALITATIVE PHYSICS. Qualitative Physics. Qualitative Physics: People know a great deal about the how the physical world works. Consider the above three situations. a) The ball will probability bounce on the ground several times an comes to rest. b) The ball will travel ARTIFICIAL INTELLIGENCE: PROBLEMSOLVING VS PLANNING. ProblemSolving Vs Planning. plans that achieve its goals, and then executes them. The limitations of the problem-. solving approach motivates the design of planning systems. can not look "inside" an operator to see how it’s defined. The goal-test predicate also is used as a. "black box" to test if a state is a goal or not. ARTIFICIAL INTELLIGENCE: MEMORY ORGANIZATION. Memory Organization: Memory is the central to commonsense behavior. Human memory contains an immense amount of knowledge about the world. Memory is also basis for learning. A system that cannot learn cannot in practice, possess common sense. A complete theory of Human memory has not yet been discovered, but we do have a number of facts at our ARTIFICIAL INTELLIGENCE: CONCEPTUAL DEPENDENCY (CD). Conceptual Dependency (CD) This representation is used in natural language processing in order to represent them earning of the sentences in such a way that inference we can be made from the sentences. It is independent of the language in which the sentences were originally stated. CD representations of a sentence is built outof primitives
ARTIFICIAL INTELLIGENCE: STRUCTURED REPRESENTATION OF A good system for the representation of structured knowledge in a particular domain should posses the following four properties: (i) Representational Adequacy:- The ability to represent all kinds of knowledge that are needed in that domain. (ii) Inferential Adequacy :- The ability to manipulate the represented structure and infer newstructures.
ARTIFICIAL INTELLIGENCE: 8 PUZZLE PROBLEM. 8 Puzzle Problem. The 8 puzzle consists of eight numbered, movable tiles set in a 3x3 frame. One cell of the frame is always empty thus making it possible to move an adjacent numbered tile into the empty cell. Such a puzzle is illustrated in following diagram. The program is to change the initial configuration into the goal configuration. ARTIFICIAL INTELLIGENCE: 2011 Artificial Intelligence is the study of how to make computers do things at which at the movement people are better. - Its main aim is depend upon the situation to take the decisions automatically. - AI is the scientific research, this research will begin ARTIFICIAL INTELLIGENCE: 2018 1)Create the entire game tree including all the terminal states. 2)For every terminal state, find out utility. This means that 1 means win and 0 means draw. 3)Apply min and max operators on the nodes of the current stage and propagate the utility value upward in the tree. 4)With the max utility value, select the action at root node using min ARTIFICIAL INTELLIGENCE: MATCHING AND LEARNING. Learning is the improvement of performance with experience over time. Learning element is the portion of a learning AI system that decides how to modify the performance element and implements those modifications. We all learn new knowledge through different methods, depending on the type of material to be learned, the amount ofrelevant
ARTIFICIAL INTELLIGENCE The thing to do is to make the best use of current situation and proceed. This is an example of an irrecoverable solution steps. 1. Ignorable problems Ex:- theorem proving. · In which solution steps can be ignored. 2. Recoverable problems Ex:- 8 puzzle. · In which solution steps can be undone. ARTIFICIAL INTELLIGENCE: QUALITATIVE PHYSICS. Qualitative Physics. Qualitative Physics: People know a great deal about the how the physical world works. Consider the above three situations. a) The ball will probability bounce on the ground several times an comes to rest. b) The ball will travel ARTIFICIAL INTELLIGENCE: PROBLEMSOLVING VS PLANNING. ProblemSolving Vs Planning. plans that achieve its goals, and then executes them. The limitations of the problem-. solving approach motivates the design of planning systems. can not look "inside" an operator to see how it’s defined. The goal-test predicate also is used as a. "black box" to test if a state is a goal or not. ARTIFICIAL INTELLIGENCE: MEMORY ORGANIZATION. Memory Organization: Memory is the central to commonsense behavior. Human memory contains an immense amount of knowledge about the world. Memory is also basis for learning. A system that cannot learn cannot in practice, possess common sense. A complete theory of Human memory has not yet been discovered, but we do have a number of facts at our ARTIFICIAL INTELLIGENCE: CONCEPTUAL DEPENDENCY (CD). Conceptual Dependency (CD) This representation is used in natural language processing in order to represent them earning of the sentences in such a way that inference we can be made from the sentences. It is independent of the language in which the sentences were originally stated. CD representations of a sentence is built outof primitives
ARTIFICIAL INTELLIGENCE: STRUCTURED REPRESENTATION OF A good system for the representation of structured knowledge in a particular domain should posses the following four properties: (i) Representational Adequacy:- The ability to represent all kinds of knowledge that are needed in that domain. (ii) Inferential Adequacy :- The ability to manipulate the represented structure and infer newstructures.
ARTIFICIAL INTELLIGENCE: 8 PUZZLE PROBLEM. 8 Puzzle Problem. The 8 puzzle consists of eight numbered, movable tiles set in a 3x3 frame. One cell of the frame is always empty thus making it possible to move an adjacent numbered tile into the empty cell. Such a puzzle is illustrated in following diagram. The program is to change the initial configuration into the goal configuration. ARTIFICIAL INTELLIGENCE: 2011 Artificial Intelligence is the study of how to make computers do things at which at the movement people are better. - Its main aim is depend upon the situation to take the decisions automatically. - AI is the scientific research, this research will begin ARTIFICIAL INTELLIGENCE: 2018 1)Create the entire game tree including all the terminal states. 2)For every terminal state, find out utility. This means that 1 means win and 0 means draw. 3)Apply min and max operators on the nodes of the current stage and propagate the utility value upward in the tree. 4)With the max utility value, select the action at root node using min ARTIFICIAL INTELLIGENCE: ARTIFICIAL INTELLIGENCE APPLICATIONS. 1. Discrete Speech Reorganization. Means People can interact with the Computer in their mother tongue. In such interaction whether they can insert time gap in between the two words or two sentences (In this type of Speech Reorganization the computer ARTIFICIAL INTELLIGENCE: ARTIFICIAL INTELLIGENCE QUESTIONS PART A – (3X20=60 Marks) Answer any THREE questions. 1.a) Define Physical Symbol System. State and pr0ve the corresponding hypothesis. (8) b)With a suitable example explain how state space approach con be used to solve AI. problems (12) 2a)With suitable examples explain about ignorable, recoverable and irrecoverable. ARTIFICIAL INTELLIGENCE: GENERATE AND TEST PROCEDURE. The generate - and - Test algorithm is a depth first search procedure because complete possible solutions are generated before test. This can be implemented states are likely to appear often in a tree; it can be implemented on a search graph rather than a tree. ARTIFICIAL INTELLIGENCE: 2018 1)Create the entire game tree including all the terminal states. 2)For every terminal state, find out utility. This means that 1 means win and 0 means draw. 3)Apply min and max operators on the nodes of the current stage and propagate the utility value upward in the tree. 4)With the max utility value, select the action at root node using min ARTIFICIAL INTELLIGENCE: PLANNING AS SEARCH Planning as Search. Planning as Search: There are two main approaches to solving planning problems, depending on the kind of search space that is explored: 1. Situation-space search. 2. Planning-space searchIn situation space search. In Situation-Space search. • the search space is the space of all possible states or situations of theworld.
ARTIFICIAL INTELLIGENCE: 2010 An expert system is software that attempts to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted. Expert systems are most common in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence (AI). A wide variety of methods can be used to simulate the performance of the expert ARTIFICIAL INTELLIGENCE: STRUCTURED REPRESENTATION OF A good system for the representation of structured knowledge in a particular domain should posses the following four properties: (i) Representational Adequacy:- The ability to represent all kinds of knowledge that are needed in that domain. (ii) Inferential Adequacy :- The ability to manipulate the represented structure and infer newstructures.
ARTIFICIAL INTELLIGENCE: KNOWLEDGE REPRESENTATION. KNOWLEDGE REPRESENTATION:-. For the purpose of solving complex problems c\encountered in AI, we need both a large amount of knowledge and some mechanism for manipulating that knowledge to create solutions to new problems. A variety of ways of representing knowledge (facts) have been exploited in AI programs. ARTIFICIAL INTELLIGENCE: KNOWLEDGE ACQUISITION BY EXPERT Definition :- Knowledge acquisition is the process of adding new knowledge to a knowledge base and refining or otherwise improving knowledge that was previously acquired. Acquisition is usually associated with some purpose such as expanding the capabilities of a system or improving its performance at some specified task. ARTIFICIAL INTELLIGENCE: 2012 A Boltzmann machine is the name given to a type of stochastic recurrent neural network by Geoffrey Hinton and Terry Sejnowski.Boltzmann machines can be seen as the stochastic, generative counterpart of Hopfield nets.They were one of the first examples of a neural network capable of learning internal representations, and are able to represent and (given sufficient time) solve difficult ARTIFICIAL INTELLIGENCE: MATCHING AND LEARNING. Learning is the improvement of performance with experience over time. Learning element is the portion of a learning AI system that decides how to modify the performance element and implements those modifications. We all learn new knowledge through different methods, depending on the type of material to be learned, the amount ofrelevant
ARTIFICIAL INTELLIGENCE The thing to do is to make the best use of current situation and proceed. This is an example of an irrecoverable solution steps. 1. Ignorable problems Ex:- theorem proving. · In which solution steps can be ignored. 2. Recoverable problems Ex:- 8 puzzle. · In which solution steps can be undone. ARTIFICIAL INTELLIGENCE: QUALITATIVE PHYSICS. Qualitative Physics. Qualitative Physics: People know a great deal about the how the physical world works. Consider the above three situations. a) The ball will probability bounce on the ground several times an comes to rest. b) The ball will travel ARTIFICIAL INTELLIGENCE: PROBLEMSOLVING VS PLANNING. ProblemSolving Vs Planning. plans that achieve its goals, and then executes them. The limitations of the problem-. solving approach motivates the design of planning systems. can not look "inside" an operator to see how it’s defined. The goal-test predicate also is used as a. "black box" to test if a state is a goal or not. ARTIFICIAL INTELLIGENCE: MEMORY ORGANIZATION. Memory Organization: Memory is the central to commonsense behavior. Human memory contains an immense amount of knowledge about the world. Memory is also basis for learning. A system that cannot learn cannot in practice, possess common sense. A complete theory of Human memory has not yet been discovered, but we do have a number of facts at our ARTIFICIAL INTELLIGENCE: CONCEPTUAL DEPENDENCY (CD). Conceptual Dependency (CD) This representation is used in natural language processing in order to represent them earning of the sentences in such a way that inference we can be made from the sentences. It is independent of the language in which the sentences were originally stated. CD representations of a sentence is built outof primitives
ARTIFICIAL INTELLIGENCE: STRUCTURED REPRESENTATION OF A good system for the representation of structured knowledge in a particular domain should posses the following four properties: (i) Representational Adequacy:- The ability to represent all kinds of knowledge that are needed in that domain. (ii) Inferential Adequacy :- The ability to manipulate the represented structure and infer newstructures.
ARTIFICIAL INTELLIGENCE: 8 PUZZLE PROBLEM. 8 Puzzle Problem. The 8 puzzle consists of eight numbered, movable tiles set in a 3x3 frame. One cell of the frame is always empty thus making it possible to move an adjacent numbered tile into the empty cell. Such a puzzle is illustrated in following diagram. The program is to change the initial configuration into the goal configuration. ARTIFICIAL INTELLIGENCE: 2011 Artificial Intelligence is the study of how to make computers do things at which at the movement people are better. - Its main aim is depend upon the situation to take the decisions automatically. - AI is the scientific research, this research will begin ARTIFICIAL INTELLIGENCE: 2018 1)Create the entire game tree including all the terminal states. 2)For every terminal state, find out utility. This means that 1 means win and 0 means draw. 3)Apply min and max operators on the nodes of the current stage and propagate the utility value upward in the tree. 4)With the max utility value, select the action at root node using min ARTIFICIAL INTELLIGENCE: MATCHING AND LEARNING. Learning is the improvement of performance with experience over time. Learning element is the portion of a learning AI system that decides how to modify the performance element and implements those modifications. We all learn new knowledge through different methods, depending on the type of material to be learned, the amount ofrelevant
ARTIFICIAL INTELLIGENCE The thing to do is to make the best use of current situation and proceed. This is an example of an irrecoverable solution steps. 1. Ignorable problems Ex:- theorem proving. · In which solution steps can be ignored. 2. Recoverable problems Ex:- 8 puzzle. · In which solution steps can be undone. ARTIFICIAL INTELLIGENCE: QUALITATIVE PHYSICS. Qualitative Physics. Qualitative Physics: People know a great deal about the how the physical world works. Consider the above three situations. a) The ball will probability bounce on the ground several times an comes to rest. b) The ball will travel ARTIFICIAL INTELLIGENCE: PROBLEMSOLVING VS PLANNING. ProblemSolving Vs Planning. plans that achieve its goals, and then executes them. The limitations of the problem-. solving approach motivates the design of planning systems. can not look "inside" an operator to see how it’s defined. The goal-test predicate also is used as a. "black box" to test if a state is a goal or not. ARTIFICIAL INTELLIGENCE: MEMORY ORGANIZATION. Memory Organization: Memory is the central to commonsense behavior. Human memory contains an immense amount of knowledge about the world. Memory is also basis for learning. A system that cannot learn cannot in practice, possess common sense. A complete theory of Human memory has not yet been discovered, but we do have a number of facts at our ARTIFICIAL INTELLIGENCE: CONCEPTUAL DEPENDENCY (CD). Conceptual Dependency (CD) This representation is used in natural language processing in order to represent them earning of the sentences in such a way that inference we can be made from the sentences. It is independent of the language in which the sentences were originally stated. CD representations of a sentence is built outof primitives
ARTIFICIAL INTELLIGENCE: STRUCTURED REPRESENTATION OF A good system for the representation of structured knowledge in a particular domain should posses the following four properties: (i) Representational Adequacy:- The ability to represent all kinds of knowledge that are needed in that domain. (ii) Inferential Adequacy :- The ability to manipulate the represented structure and infer newstructures.
ARTIFICIAL INTELLIGENCE: 8 PUZZLE PROBLEM. 8 Puzzle Problem. The 8 puzzle consists of eight numbered, movable tiles set in a 3x3 frame. One cell of the frame is always empty thus making it possible to move an adjacent numbered tile into the empty cell. Such a puzzle is illustrated in following diagram. The program is to change the initial configuration into the goal configuration. ARTIFICIAL INTELLIGENCE: 2011 Artificial Intelligence is the study of how to make computers do things at which at the movement people are better. - Its main aim is depend upon the situation to take the decisions automatically. - AI is the scientific research, this research will begin ARTIFICIAL INTELLIGENCE: 2018 1)Create the entire game tree including all the terminal states. 2)For every terminal state, find out utility. This means that 1 means win and 0 means draw. 3)Apply min and max operators on the nodes of the current stage and propagate the utility value upward in the tree. 4)With the max utility value, select the action at root node using min ARTIFICIAL INTELLIGENCE: ARTIFICIAL INTELLIGENCE APPLICATIONS. 1. Discrete Speech Reorganization. Means People can interact with the Computer in their mother tongue. In such interaction whether they can insert time gap in between the two words or two sentences (In this type of Speech Reorganization the computer ARTIFICIAL INTELLIGENCE: ARTIFICIAL INTELLIGENCE QUESTIONS PART A – (3X20=60 Marks) Answer any THREE questions. 1.a) Define Physical Symbol System. State and pr0ve the corresponding hypothesis. (8) b)With a suitable example explain how state space approach con be used to solve AI. problems (12) 2a)With suitable examples explain about ignorable, recoverable and irrecoverable. ARTIFICIAL INTELLIGENCE: GENERATE AND TEST PROCEDURE. The generate - and - Test algorithm is a depth first search procedure because complete possible solutions are generated before test. This can be implemented states are likely to appear often in a tree; it can be implemented on a search graph rather than a tree. ARTIFICIAL INTELLIGENCE: 2018 1)Create the entire game tree including all the terminal states. 2)For every terminal state, find out utility. This means that 1 means win and 0 means draw. 3)Apply min and max operators on the nodes of the current stage and propagate the utility value upward in the tree. 4)With the max utility value, select the action at root node using min ARTIFICIAL INTELLIGENCE: PLANNING AS SEARCH Planning as Search. Planning as Search: There are two main approaches to solving planning problems, depending on the kind of search space that is explored: 1. Situation-space search. 2. Planning-space searchIn situation space search. In Situation-Space search. • the search space is the space of all possible states or situations of theworld.
ARTIFICIAL INTELLIGENCE: 2010 An expert system is software that attempts to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted. Expert systems are most common in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence (AI). A wide variety of methods can be used to simulate the performance of the expert ARTIFICIAL INTELLIGENCE: STRUCTURED REPRESENTATION OF A good system for the representation of structured knowledge in a particular domain should posses the following four properties: (i) Representational Adequacy:- The ability to represent all kinds of knowledge that are needed in that domain. (ii) Inferential Adequacy :- The ability to manipulate the represented structure and infer newstructures.
ARTIFICIAL INTELLIGENCE: KNOWLEDGE REPRESENTATION. KNOWLEDGE REPRESENTATION:-. For the purpose of solving complex problems c\encountered in AI, we need both a large amount of knowledge and some mechanism for manipulating that knowledge to create solutions to new problems. A variety of ways of representing knowledge (facts) have been exploited in AI programs. ARTIFICIAL INTELLIGENCE: KNOWLEDGE ACQUISITION BY EXPERT Definition :- Knowledge acquisition is the process of adding new knowledge to a knowledge base and refining or otherwise improving knowledge that was previously acquired. Acquisition is usually associated with some purpose such as expanding the capabilities of a system or improving its performance at some specified task. ARTIFICIAL INTELLIGENCE: 2012 A Boltzmann machine is the name given to a type of stochastic recurrent neural network by Geoffrey Hinton and Terry Sejnowski.Boltzmann machines can be seen as the stochastic, generative counterpart of Hopfield nets.They were one of the first examples of a neural network capable of learning internal representations, and are able to represent and (given sufficient time) solve difficult ARTIFICIAL INTELLIGENCE: MATCHING AND LEARNING. MATCHING: So far, we have seen the process of using search to solve problems as the application of appropriate rules to individual problem states to generate new states to which the rules can then be applied, and so forth, until a solution is found. Clever search involves choosing from among the rules that can be applied at a particular point, the ones that are most likely to lead to a solution. ARTIFICIAL INTELLIGENCE: CONCEPTUAL DEPENDENCY (CD). In structured representation of knowledge CD representation is used to place the real world events or situations information on AI systems i.e to act as Intelligent Computer System., for that we using CD primitives to represent the events information on systemsknowledge-base.
ARTIFICIAL INTELLIGENCE: PROBLEMSOLVING VS PLANNING. Problem Solving vs. Planning A simple planning agent is very similar to problem-solving agents in that it constructs plans that achieve its goals, and then executes them. ARTIFICIAL INTELLIGENCE Heuristic search is a very general method applicable to a large class of problem . It includes a variety of techniques. In order to choose an appropriate method, it is necessary to analyze the problem with respect to the following considerations. ARTIFICIAL INTELLIGENCE: MEMORY ORGANIZATION. Memory Organization: Memory is the central to commonsense behavior. Human memory contains an immense amount of knowledge about the world.M
ARTIFICIAL INTELLIGENCE: QUALITATIVE PHYSICS. The goal of qualitative physics is to understand how to build and reason with abstract, number less representations. Once might object to qualitative physics on the grounds that computers are actually well suited to model physics processes. ARTIFICIAL INTELLIGENCE: 2011 where i represents the number missionaries in one side of a river . j represents the number of cannibals in the same side of river. The initial state is (3,3), that is three missionaries and three cannibals one side of a river , (Bank 1) and ( 0,0) on ARTIFICIAL INTELLIGENCE: 8 PUZZLE PROBLEM. The program is to change the initial configuration into the goal configuration. A solution to the problem is an appropriate sequence of moves, such as “move tiles 5 to the right, move tile 7 to the left ,move tile 6 to the down, etc”. ARTIFICIAL INTELLIGENCE: STRUCTURED REPRESENTATION OF Representing knowledge using logical formalism, like predicate logic, has several advantages. They can be combined with powerful inference mechanisms like resolution, which makes reasoning with facts easy. ARTIFICIAL INTELLIGENCE: 2018 Learn every thing about Artificial Intelligence here from a Subject Expert. G Veera Raghavaiah.,HOD., MCA Department., The PedanandipaduCollege
ARTIFICIAL INTELLIGENCE: MATCHING AND LEARNING. MATCHING: So far, we have seen the process of using search to solve problems as the application of appropriate rules to individual problem states to generate new states to which the rules can then be applied, and so forth, until a solution is found. Clever search involves choosing from among the rules that can be applied at a particular point, the ones that are most likely to lead to a solution. ARTIFICIAL INTELLIGENCE: CONCEPTUAL DEPENDENCY (CD). In structured representation of knowledge CD representation is used to place the real world events or situations information on AI systems i.e to act as Intelligent Computer System., for that we using CD primitives to represent the events information on systemsknowledge-base.
ARTIFICIAL INTELLIGENCE: PROBLEMSOLVING VS PLANNING. Problem Solving vs. Planning A simple planning agent is very similar to problem-solving agents in that it constructs plans that achieve its goals, and then executes them. ARTIFICIAL INTELLIGENCE Heuristic search is a very general method applicable to a large class of problem . It includes a variety of techniques. In order to choose an appropriate method, it is necessary to analyze the problem with respect to the following considerations. ARTIFICIAL INTELLIGENCE: MEMORY ORGANIZATION. Memory Organization: Memory is the central to commonsense behavior. Human memory contains an immense amount of knowledge about the world.M
ARTIFICIAL INTELLIGENCE: QUALITATIVE PHYSICS. The goal of qualitative physics is to understand how to build and reason with abstract, number less representations. Once might object to qualitative physics on the grounds that computers are actually well suited to model physics processes. ARTIFICIAL INTELLIGENCE: 2011 where i represents the number missionaries in one side of a river . j represents the number of cannibals in the same side of river. The initial state is (3,3), that is three missionaries and three cannibals one side of a river , (Bank 1) and ( 0,0) on ARTIFICIAL INTELLIGENCE: 8 PUZZLE PROBLEM. The program is to change the initial configuration into the goal configuration. A solution to the problem is an appropriate sequence of moves, such as “move tiles 5 to the right, move tile 7 to the left ,move tile 6 to the down, etc”. ARTIFICIAL INTELLIGENCE: STRUCTURED REPRESENTATION OF Representing knowledge using logical formalism, like predicate logic, has several advantages. They can be combined with powerful inference mechanisms like resolution, which makes reasoning with facts easy. ARTIFICIAL INTELLIGENCE: 2018 Learn every thing about Artificial Intelligence here from a Subject Expert. G Veera Raghavaiah.,HOD., MCA Department., The PedanandipaduCollege
ARTIFICIAL INTELLIGENCE: ARTIFICIAL INTELLIGENCE APPLICATIONS. Applications of Artificial Intelligence:- 1.Problem Solving 2.Game Playing 3.Theorem Proving 4.Natural Language Processing & Understand ARTIFICIAL INTELLIGENCE: ARTIFICIAL INTELLIGENCE QUESTIONS Learn every thing about Artificial Intelligence here from a Subject Expert. G Veera Raghavaiah.,HOD., MCA Department., The PedanandipaduCollege
ARTIFICIAL INTELLIGENCE: GENERATE AND TEST PROCEDURE. The generate - and - Test algorithm is a depth first search procedure because complete possible solutions are generated before test. This can be implemented states are likely to appear often in a tree; it can be implemented on a search graph rather than a tree. ARTIFICIAL INTELLIGENCE: 2010 An expert system is software that attempts to provide an answer to a problem, or clarify uncertainties where normally one or more human experts would need to be consulted. Expert systems are most common in a specific problem domain, and is a traditional application and/or subfield of artificial intelligence (AI). A wide variety of methods can be used to simulate the performance of the expert ARTIFICIAL INTELLIGENCE: PLANNING AS SEARCH Principle of Least Commitment:The principle of least commitment is the idea of never making a choice unless required to do so. The advantage ARTIFICIAL INTELLIGENCE: KNOWLEDGE REPRESENTATION. One common representation is natural language (particularly English) sentences. Regardless of the representation for facts we use in a program , we may also need to be concerned with an English representation of those facts in order to facilitate getting ARTIFICIAL INTELLIGENCE: 2018 Learn every thing about Artificial Intelligence here from a Subject Expert. G Veera Raghavaiah.,HOD., MCA Department., The PedanandipaduCollege
ARTIFICIAL INTELLIGENCE: KNOWLEDGE ACQUISITION BY EXPERT To be effective, the newly acquired knowledge should be integrated with existing knowledge in some meaningful way so that nontrivial inferences can be drawn from the resultant body of knowledge . the knowledge should, of course, be accurate, non redundant, consistent(non contradictory ), and fairly complete in the sense that it is possible to reliably reason about many of the important ARTIFICIAL INTELLIGENCE: STRUCTURED REPRESENTATION OF Representing knowledge using logical formalism, like predicate logic, has several advantages. They can be combined with powerful inference mechanisms like resolution, which makes reasoning with facts easy. ARTIFICIAL INTELLIGENCE: 2012 A Boltzmann machine is the name given to a type of stochastic recurrent neural network by Geoffrey Hinton and Terry Sejnowski.Boltzmann machines can be seen as the stochastic, generative counterpart of Hopfield nets.They were one of the first examples of a neural network capable of learning internal representations, and are able to represent and (given sufficient time) solve difficult ARTIFICIAL INTELLIGENCE Learn every thing about Artificial Intelligence here from a Subject Expert. G Veera Raghavaiah.,HOD., MCA Department., The Pedanandipadu College of Arts & Sciences., Pedanandipadu-522235. Guntur-Dt., Andhrapradesh., India. FRIDAY, SEPTEMBER 28, 2018 MINI MAX SEARCH PROCEDURE. * The Min-Max algorithm evaluates decision-based on the current situation of the game. * This algorithm needs a deterministic environment with exactinformation.
* Min-Max algorithm directly implements the defining equation. * Every time based on the successor state min-max value is calculated using simple recursive computation.* Algorithm:
1)Create the entire game tree including all the terminal states. 2)For every terminal state, find out utility. This means that 1 means win and 0 means draw. 3)Apply min and max operators on the nodes of the current stage and propagate the utility value upward in the tree. 4)With the max utility value, select the action at root node usingmin-max decision.
* Example,
First iteration Procedure:Min= inf
Min(inf,10)=10
Min(10,11)=10
Min=inf
Min(inf,9)=9
Min(9,11)=9
.
.
Second iteration Procedure:Max=-inf
Max(-inf,10)=10
Max(10,9)=10
Max=-inf
Max(-inf,14)=14
Max(14,13)=14
.
.
Again Min Procedure:Min=inf
Min(inf,10)=10
Min(10,14)=10
.
.
.
If we keep going, the final answer will be 10. Posted by Gurram. Veera Raghavavaiah at 11:54 PM0 comments
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