Ch01 software and software engineering
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The document discusses the dual roles and definitions of software. It describes software as both a product that delivers computing potential and a vehicle that supports system functionality. Software is defined as a set of programs, documents, and data that form a configuration. The document also lists different categories of software applications such as system software, application software, embedded software, and web applications. It discusses challenges with legacy software and myths about software that can lead to bad decisions if believed.
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3. Software evolves and deteriorates over time unlike hardware, which wears out. Most software continues to be custom built despite a slow trend toward component-based construction. Maintaining and evolving legacy software poses challenges.
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This document discusses the nature of software. It defines software as a set of instructions that can be stored electronically. Software engineering encompasses processes and methods to build high quality computer software. Software has a dual role as both a product and a vehicle to deliver products. Characteristics of software include being engineered rather than manufactured, and not wearing out over time like hardware. Software application domains include system software, application software, engineering/scientific software, embedded software, product-line software, web applications, and artificial intelligence software. The document also discusses challenges like open-world computing and legacy software.
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1. Software is defined as computer programs and documents that are designed by software engineers. Software is logical rather than physical and does not wear out over time like hardware.
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The document discusses several key aspects of software and software engineering:
1. Software serves both as a product that transforms information and as a vehicle that delivers computing capabilities. It controls programs, enables communications, and helps build other software.
2. Software is more complex and difficult to develop than hardware but easier to modify and upgrade. Software costs are concentrated in design rather than production.
3. Software evolves and deteriorates over time unlike hardware, which wears out. Most software continues to be custom built despite a slow trend toward component-based construction. Maintaining and evolving legacy software poses challenges.
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1. Software serves both as a product that transforms information and as a vehicle that delivers computing capabilities. It controls programs, enables communications, and helps build other software.
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This document discusses knowledge-based agents in artificial intelligence. It defines knowledge-based agents as agents that maintain an internal state of knowledge, reason over that knowledge, update their knowledge based on observations, and take actions. Knowledge-based agents have two main components: a knowledge base that stores facts about the world, and an inference system that applies logical rules to deduce new information from the knowledge base. The document also describes the architecture of knowledge-based agents and different approaches to designing them.
Rule based system
A rule-based system uses predefined rules to make logical deductions and choices to perform automated actions. It consists of a database of rules representing knowledge, a database of facts as inputs, and an inference engine that controls the process of deriving conclusions by applying rules to facts. A rule-based system mimics human decision making by applying rules in an "if-then" format to incoming data to perform actions, but unlike AI it does not learn or adapt on its own.
Formal Logic in AI
This document discusses formal logic and its applications in AI and machine learning. It begins by explaining why logic is useful in complex domains or with little data. It then describes logic-based approaches to AI that use symbolic reasoning as an alternative to machine learning. The document proceeds to explain propositional logic and first-order logic, noting how first-order logic improves on propositional logic by allowing variables. It also mentions other logics and their applications in areas like automated discovery, inductive programming, and verification of computer systems and machine learning models.
Production based system
The document discusses production systems, which are rule-based systems used in artificial intelligence to model intelligent behavior. A production system consists of a global database, set of production rules, and control system. The rules fire to modify the database based on conditions. Different control strategies are used to determine which rules fire. Production systems are modular and allow knowledge representation as condition-action rules. Examples of applications in problem solving are provided.
Game playing in AI
The document discusses game playing in artificial intelligence. It describes how general game playing (GGP) involves designing AI that can play multiple games by learning the rules, rather than being programmed for a specific game. The document outlines how the minimax algorithm is commonly used for game playing, involving move generation and static evaluation functions to search game trees and determine the best move by maximizing or minimizing values at each level.
Diagnosis test of diabetics and hypertension by AI
A study on “Diagnosis Test of Diabetics and Hypertension by AI”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on “impact of artificial intelligence in covid19 diagnosis”
A study on “Impact of Artificial Intelligence in COVID-19 Diagnosis”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on “impact of artificial intelligence in covid19 diagnosis”
Although the lungs are one of the most vital organs in the body, they are vulnerable to infection and injury. COVID-19 has put the entire world in an unprecedented difficult situation, bringing life to a halt and claiming thousands of lives all across the world. Medical imaging, such as X-rays and computed tomography (CT), is essential in the global fight against COVID-19, and newly emerging artificial intelligence (AI) technologies are boosting the power of imaging tools and assisting medical specialists. AI can improve job efficiency by precisely identifying infections in X-ray and CT images and allowing further measurement. We focus on the integration of AI with X-ray and CT, both of which are routinely used in frontline hospitals, to reflect the most recent progress in medical imaging and radiology combating COVID-19.
More from Dr. C.V. Suresh Babu (20)
Diagnosis test of diabetics and hypertension by AI
Diagnosis test of diabetics and hypertension by AI
A study on “impact of artificial intelligence in covid19 diagnosis”
A study on “impact of artificial intelligence in covid19 diagnosis”
A study on “impact of artificial intelligence in covid19 diagnosis”
A study on “impact of artificial intelligence in covid19 diagnosis”
Ch01 software and software engineering
- 1. Chapter 1 Software and Software Engineering
- 3. What is Software? Software is a set of items or objects that form a “configuration” that includes • programs • documents • data ...