Foundations of Intelligent Systems: A Comprehensive Guide to the Fundamentals

Intelligent systems are computer systems that have the ability to learn, reason, and make decisions in a way that mimics human intelligence. They are used in a wide range of applications, including natural language processing, computer vision, robotics, and decision support.

The foundations of intelligent systems are based on a number of disciplines, including computer science, mathematics, and psychology. This article provides a comprehensive overview of the foundations of intelligent systems, covering key concepts, methods, and applications.

The following are some of the key concepts that underlie the design and development of intelligent systems:

Foundations of Intelligent Systems: 23rd International Symposium, ISMIS 2024, Warsaw, Poland, June 26-29, 2024, Proceedings (Lecture Notes in Computer Science Book 10352)

by Alessandro Manzoni

4.3 out of 5

Language	:	English
File size	:	22493 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	776 pages
Paperback	:	118 pages
Item Weight	:	6.1 ounces
Dimensions	:	6 x 0.27 x 9 inches
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• Knowledge representation: Intelligent systems must be able to represent knowledge in a way that can be processed by a computer. This can be done using a variety of methods, such as logic, rules, or frames.
• Reasoning: Intelligent systems must be able to reason about the knowledge that they have in order to make decisions and solve problems. This can be done using a variety of methods, such as forward chaining, backward chaining, or abductive reasoning.
• Problem solving: Intelligent systems must be able to solve problems in a way that is efficient and effective. This can be done using a variety of methods, such as search algorithms, heuristic methods, or constraint satisfaction.
• Decision making: Intelligent systems must be able to make decisions in a way that is rational and well-informed. This can be done using a variety of methods, such as decision theory, utility theory, or game theory.
• Natural language processing: Intelligent systems must be able to understand and generate natural language. This can be done using a variety of methods, such as statistical language models, machine translation, or natural language generation.
• Computer vision: Intelligent systems must be able to interpret visual data. This can be done using a variety of methods, such as image processing, object recognition, or scene understanding.
• Robotics: Intelligent systems must be able to control robots in a way that is safe and efficient. This can be done using a variety of methods, such as motion planning, control theory, or computer vision.

The following are some of the most common methods used to develop intelligent systems:

• Machine learning: Machine learning is a type of artificial intelligence that allows computers to learn from data without being explicitly programmed. Machine learning algorithms can be used to solve a variety of problems, such as classification, regression, and clustering.
• Knowledge-based systems: Knowledge-based systems are a type of intelligent system that uses explicit knowledge to solve problems. Knowledge-based systems can be used to solve a variety of problems, such as diagnosis, planning, and decision making.
• Hybrid systems: Hybrid systems are a type of intelligent system that combines machine learning and knowledge-based techniques. Hybrid systems can be used to solve a variety of problems, such as natural language processing, computer vision, and robotics.

Intelligent systems are used in a wide range of applications, including:

• Natural language processing: Intelligent systems can be used to understand and generate natural language. This can be used for a variety of applications, such as machine translation, text summarization, and question answering.
• Computer vision: Intelligent systems can be used to interpret visual data. This can be used for a variety of applications, such as object recognition, scene understanding, and facial recognition.
• Robotics: Intelligent systems can be used to control robots in a way that is safe and efficient. This can be used for a variety of applications, such as manufacturing, healthcare, and space exploration.
• Decision support: Intelligent systems can be used to provide decision support to humans. This can be used for a variety of applications, such as medical diagnosis, financial planning, and military planning.

Intelligent systems are a powerful tool that can be used to solve a wide range of problems. The foundations of intelligent systems are based on a number of disciplines, including computer science, mathematics, and psychology. This article has provided a comprehensive overview of the foundations of intelligent systems, covering key concepts, methods, and applications.

Foundations of Intelligent Systems: 23rd International Symposium, ISMIS 2024, Warsaw, Poland, June 26-29, 2024, Proceedings (Lecture Notes in Computer Science Book 10352)

by Alessandro Manzoni

4.3 out of 5

Language	:	English
File size	:	22493 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	776 pages
Paperback	:	118 pages
Item Weight	:	6.1 ounces
Dimensions	:	6 x 0.27 x 9 inches
Screen Reader	:	Supported

FREE