AI and Related Fields
AI and related fields: Language and understanding refers to the ability to "understand" and respond to the natural language. It includes to translate from spoken language to a written form and to translate from one natural language to another natural language. Types of language understanding are Speech Understanding, Semantic Information Processing (Computational Linguistics), Question Answering, Information Retrieval and Language Translation. Learning and adaptive systems refers to the ability to adapt behavior based on previous experience and to develop general rules concerning the world based on such experience. Some examples are: Cybernetics and Concept Formation. Problem solving is the ability to formulate a problem in a suitable representation and to plan for its solution and to know when new information is needed and how to obtain it. Types are Inference (Resolution-Based Theorem Proving, Plausible Inference and Inductive Inference), Interactive Problem Solving, Automatic Program Writing and Heuristic Search. The ability to analyze a sensed scene by relating it to an internal model which represents the perceiving organism's "knowledge of the world" is understood as perception. The result of the above analysis is a structured set of relationships between the entities in the scene. The ability to develop an internal representation and set of transformation rules that can be used to predict the behavior and the relationship between some set of real-world objects or entities is collectively known as modeling. A robot is an overall combination of most or all of the above abilities with the ability to move over terrain and manipulate objects. The ability to accept a formal set of rules for games such as Chess, Go, Kalah, Checkers, etc. and to translate these rules into a representation or structure which allows problem-solving and learning abilities to be used in reaching an adequate level of performance.
Summary
AI and related fields: Language and understanding refers to the ability to "understand" and respond to the natural language. It includes to translate from spoken language to a written form and to translate from one natural language to another natural language. Types of language understanding are Speech Understanding, Semantic Information Processing (Computational Linguistics), Question Answering, Information Retrieval and Language Translation. Learning and adaptive systems refers to the ability to adapt behavior based on previous experience and to develop general rules concerning the world based on such experience. Some examples are: Cybernetics and Concept Formation. Problem solving is the ability to formulate a problem in a suitable representation and to plan for its solution and to know when new information is needed and how to obtain it. Types are Inference (Resolution-Based Theorem Proving, Plausible Inference and Inductive Inference), Interactive Problem Solving, Automatic Program Writing and Heuristic Search. The ability to analyze a sensed scene by relating it to an internal model which represents the perceiving organism's "knowledge of the world" is understood as perception. The result of the above analysis is a structured set of relationships between the entities in the scene. The ability to develop an internal representation and set of transformation rules that can be used to predict the behavior and the relationship between some set of real-world objects or entities is collectively known as modeling. A robot is an overall combination of most or all of the above abilities with the ability to move over terrain and manipulate objects. The ability to accept a formal set of rules for games such as Chess, Go, Kalah, Checkers, etc. and to translate these rules into a representation or structure which allows problem-solving and learning abilities to be used in reaching an adequate level of performance.
Things to Remember
- Language and understanding refers to the ability to "understand" and respond to the natural language. It includes to translate from spoken language to a written form and to translate from one natural language to another natural language.
- Types of language understanding are Speech Understanding, Semantic Information Processing (Computational Linguistics), Question Answering, Information Retrieval and Language Translation.
- Learning and adaptive systems refers to the ability to adapt behavior based on previous experience and to develop general rules concerning the world based on such experience. Some examples are: Cybernetics and Concept Formation.
- Problem solving is the ability to formulate a problem in a suitable representation and to plan for its solution and to know when new information is needed and how to obtain it.
- The ability to analyze a sensed scene by relating it to an internal model which represents the perceiving organism's "knowledge of the world" is understood as perception.
- The ability to develop an internal representation and set of transformation rules that can be used to predict the behavior and the relationship between some set of real-world objects or entities is collectively known as modeling.
- A robot is an overall combination of most or all of the above abilities with the ability to move over terrain and manipulate objects.
- The ability to accept a formal set of rules for games such as Chess, Go, Kalah, Checkers, etc. and to translate these rules into a representation or structure which allows problem-solving and learning abilities to be used in reaching an adequate level of performance.
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AI and Related Fields
AI and Related fields
A. Language understanding:
It refers to the ability to "understand" and respond to the natural language. It includes to translate from spoken language to a written form and to translate from one natural language to another natural language.
- Speech Understanding
- Semantic Information Processing (Computational Linguistics)
- Question Answering
- Information Retrieval
- Language Translation
B. Learning and adaptive systems:
It refers to the ability to adapt behavior based on previous experience and to develop general rules concerning the world based on such
experience. Some examples are:
- Cybernetics
- Concept Formation
C. Problem solving:
The ability to formulate a problem in a suitable representation and to plan for its solution and to know when new information is needed and how to obtain it.
- Inference (Resolution-Based Theorem Proving, Plausible Inference and Inductive Inference)
- Interactive Problem Solving
- Automatic Program Writing
- Heuristic Search
D. Perception (visual):
The ability to analyze a sensed scene by relating it to an internal model which represents the perceiving organism's "knowledge of the world" is understood as perception. The result of the above analysis is a structured set of relationships between the entities in the scene.
- Pattern Recognition
- Scene Analysis
E. Modeling:
The ability to develop an internal representation and set of transformation rules that can be used to predict the behavior and the relationship between some set of real-world objects or entities is collectively known as modeling.
- The Representation Problem for Problem Solving Systems
- Modeling Natural Systems (Economic, Sociological, Ecological, Biological etc.)
- Hobot World Modeling (Perceptual and Functional Representations).
F. Robots:
A robot is an overall combination of most or all of the above abilities with the ability to move over terrain and manipulate objects.
- Exploration
- Transportation/Navigation
- Industrial Automation (e.g., Process Control, Assembly Tasks, Executive Tasks)
- Security
- Other (Agriculture, Fishing, Mining, Sanitation,Construction, etc.)
- Military
- Household
G. Games:
The ability to accept a formal set of rules for games such as Chess, Go, Kalah, Checkers, etc. and to translate these rules into a representation or structure which allows problem-solving and learning abilities to be used in reaching an adequate level of performance.
- Particular Games (Chess, Go, Bridge, etc).
References:
- Elaine Rich, Kevin Knight 1991, "Artificial Intelligence".
- Nilsson, Nils J. Principles of Artificial Intelligence, Narosa Publishing House New Delhi, 1998.
- Norvis, Peter & Russel, Stuart Artificial Intelligence: A modern Approach, Prentice Hall, NJ, 1995
- Patterson, Dan W. Introduction to Artificial Intelligence and Expert Systems, Prentice Hall of India Private Limited New Delhi, 1998
Lesson
Introduction
Subject
Computer Engineering
Grade
Engineering
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