Tujuan Kecerdasan Buatan(ai) Adalah Sebagai Berikut
Tujuan Kecerdasan Buatan(ai) Adalah Sebagai Berikut – Provides an understanding of AI problems and problem-solving methods, including: state-space representations, state diagrams, trace trees, AND/OR trees, localization, and search method search
A system using artificial intelligence will provide output in the form of a solution to a problem based on an existing collection of knowledge. In Figure 1 of the AI system, the input is given in the form of questions. The system must be equipped with an existing set of knowledge in the knowledge base. The system must have an inference engine capable of drawing conclusions based on facts or knowledge. The output given is in the form of a solution to the problem generated by inference.
Tujuan Kecerdasan Buatan(ai) Adalah Sebagai Berikut
Derivation input of automobile knowledge base: Problem, Questions, etc. Output: answer, solution, etc. Computer Figure 1. AI System Components
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In general, there are 4 phases to building an AI system 1. Define the problem accurately. The definition includes precise specifications about the initial state (initial state) and the desired solution (goal state) 2. Analyze the problem and find an appropriate problem-solving technique 3. Provide the knowledge needed to solve the problem 4. Choose the best solution technique for a problem
For example, a chess game problem must be determined: 1. The starting position on the board The starting position of each game is always the same, that is, all the pieces are placed on both sides of the board, ie. White side and black side. 2. Rules of moves These rules are very useful for determining the moves of the pieces, that is, going from one state to another. For example, to make it easier to show the positions of the pieces, each square is represented horizontally by letters (a, b, c, d, e, f, g, h) and numbers (1, 2, 3). ) means, 4, 5, 6, 7, 8) in the vertical direction.
The rule for moving a pawn from position (e, 2) to (e, 4) can be shown by the following rule: If the white pawns in squares (e, 2) and squares (e, 3) are empty and the squares are (e, 4) is empty, then move the (e, 2) piece to (e, 4) 3. Goal achieved The goal is the position on the board that shows that you have defeated your opponent. The victory featured the king’s position, which he could no longer move.
This example shows the representation of a problem in a state space, which is a space containing all possible states. We can start a game of chess by placing ourselves in an initial state, then move from one state to another according to existing rules, and end the game when the goal is reached.
Apa Itu Kecerdasan Buatan
9 Problem Statement To properly describe the problem, you must: 1. Define a state space 2. Define one or more initial conditions 3. Define one or more goal states 4. Define a set of rules
There are several ways of representing a state space, including: 1. State graph A graph consists of nodes representing states, namely the initial state and the new state to be reached with the help of operators. Nodes in a state graph are connected using arcs with arrows to indicate the direction from one state to the next.
11 State Diagram Example Figure 2 is a state diagram where node M represents the initial state and node T is the destination. There are 4 paths from M to T: M-A-B-C-E-T 2. M-A-B-C-E-H-T 3. M-D-C-E-T 4. M-D-C-E-H-T Figure 2 state diagram
There is no dead end or path to the destination: M-A-B-C-E-F-G M-A-B-C-E-I-J M-D-C-E-F-G M-D-C-E-I-J M-D-I-J Figure 2 State Diagram
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A tree structure is used to describe situations hierarchically. The node at level-0 is called “root”. Root node: shows the initial state, has several branches, and consists of several nodes called “children”. Nodes with no children are called “leaves” and represent the end of the search, either a goal or a dead end. The figure below shows a search tree for a statechart with 6 levels.
AND/OR TREES Problem M has several possible solutions, namely A OR B OR C OR D. Problem M can only be solved by A AND B AND C AND D
15 AND/OR TREE Cont…2 Example: By using an AND/OR tree, the goal achieved in the tree in the above figure can be simplified to level-2, as shown in the figure below.
Example 1: Bucket problem There are 2 buckets (A and B), bucket A has a capacity of 4 liters and bucket B has a capacity of 3 liters. Then there is a water pump for filling the bucket with water. How can you fill a 4 liter bucket with 2 liters of water? Solution: 1. Identification of state space This problem can be described by a set of integers: x = bucket A is filled with 4 liters of water y = bucket B is filled with 3 liters of water Let space = (x , y ) such that x ∈ and y ∈
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Initial state: both buckets are empty = (0, 0) Goal: 2 liters of water in a 4 liter bucket = (2, n) any n 3. Bucket state The bucket state can be described as follows:
Suppose we could pump up the buckets to fill them up, dump the water out of the buckets, and dump the water in one bucket into the other. We have developed some rules, which can be described as follows:
21 Solutions: Continued…5 5. Representing the State Space as a Trace Tree The search for a solution can be described by a tree. Each node shows a state. A path from parent to child represents 1 operation. Each node has child nodes that represent states reachable from the parent node.
25 Task 2 1 Complete the above detection tree for each possibility, so as to achieve goal 2, and then make two goal solution tables for the goal conditions, namely solution 3 and solution 4. collect next week
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In order for this website to function properly, we record user data and share it with processors. To use this site, you must agree to our Privacy Policy, including cookie policy JAKARTA – In recent years the term artificial intelligence (AI) has been used increasingly on various topics of discussion. This goes hand-in-hand with the growing role of technology in the industry and the rise of OpenAI with its Dall-E and ChatGPT. While artificial intelligence is necessary to help people. However, not everyone understands its meaning and history
It appeared more than ten years ago. It was 2004, and American computer scientist and cognitive scientist John McCarthy, born September 4, 1927, explained the definition of artificial intelligence in his article.
It is the science and technology of creating intelligent machines, especially intelligent computer programs. It is related to the same task of understanding human intelligence using computers, but AI need not limit itself to biologically observable methods.
Long before John McCarthy, computer scientist and mathematician Alan Turing discussed the concept of artificial intelligence. in his title
Solution: Soal Vclass Ai Pert 1 14
It is reportedly an important part of the history of artificial intelligence and a persistent concept in philosophy because of its use of ideas surrounding linguistics.
Russell and Norvig saw the potential for artificial intelligence to differentiate itself from today’s computer systems. According to the book, artificial intelligence will lead to the ability to think like a human. Both noted some potential for artificial intelligence, namely in systems that think like humans and act like humans.
But before they go there, the two experts propose a more ideal and concrete solution, that of becoming a system that thinks and acts rationally. In the context of AI, there are
Simply put, artificial intelligence is the combination of computer science and powerful data to solve human problems.
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In fact, the original idea of a “thinking machine” appeared as early as 2000. From idea, to experiment, to development, AI has experienced rapid development in this century.
Then in 1956, John McCarthy gave a definition of the term artificial intelligence at the first artificial intelligence conference held at Dartmouth College. That same year, other computer scientists such as Allen Newel, J.C. Shaw, and Herbert Simon created the first artificial intelligence software program, the
The first neural network-based computer was developed in 1967, called the Mark 1 Perceptron. Also in the same year, Marvin Minsky and Seymour Papert, cognitive scientists in the field of artificial intelligence in the United States, published a book called
.This book is an important work in the field of neural networks, applicable to various neural research projects in the future.
Kecerdasan Buatan (ai) Itu Apa?
For neural network training. It works by propagating the error at the output of the neural network back to the input through the various layers in the network, thereby updating the weights and biases in the network.
In 1997, an event related to artificial intelligence also shocked mankind. The artificial intelligence system “Deep Blue” developed by IBM defeated the world chess champion Garry Kasparov in a human-computer chess series.
Deep Blue uses the negamax search algorithm, which allows the engine to perform rapid analysis and make accurate decisions. Deep Blue combines parallel processing techniques with heuristic algorithms and chess strategy to make game decisions. The system is capable of processing millions of possible moves in less than a second.
A machine-versus-human race followed again in 2011, with IBM Watson’s artificial intelligence system successfully demonstrating analytical and natural abilities.
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They answer questions in various categories while in danger! Against champions Ken Jennings and Brad Rutter. IBM Watson is the winner.
Danger! It is a very difficult task for an artificial intelligence system because it requires the ability to understand natural language and solve complex problems. However, IBM Watson managed to do the job
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