This presentation discusses about the following topics:
Hybrid Systems
Hybridization
Combinations
Comparison of Expert Systems, Fuzzy Systems, Neural Networks and Genetic Algorithms
Current Progress
Primary Components
MultiComponents
Degree of Integration
Transformational, hierarchial and integrated
Stand Alone Models
Integrated – Fused Architectures
Generalized Fused Framework
System Types for Hybridization
Soft computing is an approach to computing that aims to model human-like decision making. It deals with imprecise or uncertain data using techniques like fuzzy logic, neural networks, and genetic algorithms. The goal is to develop systems that are tolerant of imprecision, uncertainty, and approximation to achieve practical and low-cost solutions to real-world problems. Soft computing was initiated in 1981 and includes fields like fuzzy logic, neural networks, and evolutionary computation. It provides approximate solutions using techniques like neural network reasoning, genetic programming, and functional approximation.
The document discusses procedural versus declarative knowledge representation and how logic programming languages like Prolog allow knowledge to be represented declaratively through logical rules. It also covers topics like forward and backward reasoning, matching rules to facts in working memory, and using control knowledge to guide the problem solving process. Logic programming represents knowledge through Horn clauses and uses backward chaining inference to attempt to prove goals.
This presentation discusses the following ANN concepts:
Introduction
Characteristics
Learning methods
Taxonomy
Evolution of neural networks
Basic models
Important technologies
Applications
Soft computing is an emerging approach to computing that aims to mimic human reasoning and learning in uncertain and imprecise environments. It includes neural networks, fuzzy logic, and genetic algorithms. The main goals of soft computing are to develop intelligent machines to solve real-world problems that are difficult to model mathematically, while exploiting tolerance for uncertainty like humans. Some applications of soft computing include consumer appliances, robotics, food preparation devices, and game playing. Soft computing is well-suited for problems not solvable by traditional computing due to its characteristics of tractability, low cost, and high machine intelligence.
This document provides an introduction to soft computing. It discusses intelligent systems and how traditional approaches use mathematical models and rule-based systems. Soft computing aims to mimic human reasoning using fuzzy systems, neural networks, evolutionary computing, and probabilistic reasoning. Soft computing is tolerant of imprecision, uncertainty, partial truths, and approximations. It has advantages over hard computing by being closer to human thinking and using linguistic models that are simple, comprehensible, and fast. Soft computing has become widely used with over 24,000 publications to date.
This document discusses defuzzification in fuzzy logic. It defines defuzzification as the process of converting fuzzy quantities into crisp quantities. There are several reasons for and applications of defuzzification, such as converting fuzzy controller outputs into crisp values for applications. The document outlines the defuzzification process and several common defuzzification methods, including the centroid method, weighted average method, and max membership principle. It also discusses the lambda-cut and alpha-cut methods for deriving crisp values from fuzzy sets and relations.
Defuzzification is the process of producing a quantifiable result in Crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.
Soft computing is an approach to computing that aims to model human-like decision making. It deals with imprecise or uncertain data using techniques like fuzzy logic, neural networks, and genetic algorithms. The goal is to develop systems that are tolerant of imprecision, uncertainty, and approximation to achieve practical and low-cost solutions to real-world problems. Soft computing was initiated in 1981 and includes fields like fuzzy logic, neural networks, and evolutionary computation. It provides approximate solutions using techniques like neural network reasoning, genetic programming, and functional approximation.
The document discusses procedural versus declarative knowledge representation and how logic programming languages like Prolog allow knowledge to be represented declaratively through logical rules. It also covers topics like forward and backward reasoning, matching rules to facts in working memory, and using control knowledge to guide the problem solving process. Logic programming represents knowledge through Horn clauses and uses backward chaining inference to attempt to prove goals.
This presentation discusses the following ANN concepts:
Introduction
Characteristics
Learning methods
Taxonomy
Evolution of neural networks
Basic models
Important technologies
Applications
Soft computing is an emerging approach to computing that aims to mimic human reasoning and learning in uncertain and imprecise environments. It includes neural networks, fuzzy logic, and genetic algorithms. The main goals of soft computing are to develop intelligent machines to solve real-world problems that are difficult to model mathematically, while exploiting tolerance for uncertainty like humans. Some applications of soft computing include consumer appliances, robotics, food preparation devices, and game playing. Soft computing is well-suited for problems not solvable by traditional computing due to its characteristics of tractability, low cost, and high machine intelligence.
This document provides an introduction to soft computing. It discusses intelligent systems and how traditional approaches use mathematical models and rule-based systems. Soft computing aims to mimic human reasoning using fuzzy systems, neural networks, evolutionary computing, and probabilistic reasoning. Soft computing is tolerant of imprecision, uncertainty, partial truths, and approximations. It has advantages over hard computing by being closer to human thinking and using linguistic models that are simple, comprehensible, and fast. Soft computing has become widely used with over 24,000 publications to date.
This document discusses defuzzification in fuzzy logic. It defines defuzzification as the process of converting fuzzy quantities into crisp quantities. There are several reasons for and applications of defuzzification, such as converting fuzzy controller outputs into crisp values for applications. The document outlines the defuzzification process and several common defuzzification methods, including the centroid method, weighted average method, and max membership principle. It also discusses the lambda-cut and alpha-cut methods for deriving crisp values from fuzzy sets and relations.
Defuzzification is the process of producing a quantifiable result in Crisp logic, given fuzzy sets and corresponding membership degrees. It is the process that maps a fuzzy set to a crisp set. It is typically needed in fuzzy control systems.
Expert Systems are computer programs that use knowledge and inference procedures to solve problems that normally require human expertise. They are designed to solve problems at an expert level by accessing a substantial knowledge base and applying reasoning mechanisms. Typical tasks for expert systems include data interpretation, diagnosis, structural analysis, planning, and prediction. Expert systems consist of a knowledge base, inference engine, user interface, knowledge acquisition system, and explanation facility. The inference engine applies rules and reasoning to the knowledge base to solve problems. Knowledge acquisition involves eliciting expertise from human experts to build the knowledge base.
This document presents a presentation on fuzzy expert systems. It introduces expert systems and how they combine human expertise with computational capabilities. It then discusses the evolution of fuzzy expert systems to handle imprecision and uncertainty. The key components of a fuzzy expert system are described, including the knowledge base, inference engine, and user interface. Steps for constructing a fuzzy expert system are outlined, from knowledge representation to testing rules. Pros and cons as well as applications in various domains like agriculture, education, and medicine are also summarized.
Fuzzy logic was introduced by Lotfi Zadeh in 1965 to address problems with classical logic being too precise. Fuzzy logic allows for truth values between 0 and 1 rather than binary true/false. It involves fuzzy sets, membership functions, linguistic variables, and fuzzy rules. Fuzzy logic can be applied to knowledge representation and inference using concepts like fuzzy predicates, relations, modifiers and quantifiers. It has various applications including household appliances, animation, industrial automation, and more.
Soft computing (ANN and Fuzzy Logic) : Dr. Purnima PanditPurnima Pandit
The document discusses soft computing and its techniques, including artificial neural networks (ANN). It provides an overview of ANN, including how biological neurons inspired the basic ANN model. A neuron has inputs, outputs, weights, and an activation function. Networks can be single or multilayer. Learning involves updating weights to minimize error, with backpropagation commonly used for multilayer networks. Applications include pattern recognition, function approximation, and parameter estimation. A simple example is provided to estimate the slope and intercept of a line using ANN.
This document discusses applications of soft computing techniques. It describes some key advantages of soft computing like modeling human reasoning and being comprehensible. It then outlines several applications of neural networks, fuzzy logic, and genetic algorithms in areas like control, business, finance, and others. Specific examples of soft computing applications covered in the chapter include combining multispectral and SAR images for flood analysis, using a genetic algorithm to optimize the traveling salesman problem, and developing soft computing based hybrid fuzzy controllers and rocket engine control systems.
This document provides an introduction to soft computing techniques including fuzzy logic, neural networks, and genetic algorithms. It discusses how these techniques are inspired by human intelligence and can handle imprecise or uncertain data. Examples of applications are given such as fuzzy logic in washing machines to optimize the washing process based on sensor readings, and using genetic algorithms to design optimal robotics.
Fuzzy inference systems use fuzzy logic to map inputs to outputs. There are two main types:
Mamdani systems use fuzzy outputs and are well-suited for problems involving human expert knowledge. Sugeno systems have faster computation using linear or constant outputs.
The fuzzy inference process involves fuzzifying inputs, applying fuzzy logic operators, and using if-then rules. Outputs are determined through implication, aggregation, and defuzzification. Mamdani systems find the centroid of fuzzy outputs while Sugeno uses weighted averages, making it more efficient.
This document provides an overview of fuzzy logic concepts for a course on soft computing. It discusses key fuzzy logic topics like membership functions, fuzzy sets, linguistic variables, fuzzy rules, fuzzy inference, and neuro-fuzzy systems. The document also provides examples of commonly used membership functions like triangular, trapezoidal, and Gaussian functions. It explains how fuzzy logic allows for approximate reasoning using natural language terms and multivalent logic with membership values between 0 and 1.
An knowledge based system (KBS) is a type of artificial intelligence program that uses a knowledge base to solve problems within a specialized domain that normally requires human expertise. A KBS consists of a knowledge base containing facts, rules, and heuristics about its domain, an inference engine that applies reasoning to the knowledge base, and a user interface. The knowledge base is developed by a knowledge engineer working with a domain expert to capture their expertise. A KBS can perform tasks like classification, diagnosis and planning by drawing on the captured knowledge through its inference engine.
Neuro-fuzzy systems combine neural networks and fuzzy logic to overcome the limitations of each. They were created to achieve the mapping precision of neural networks and the interpretability of fuzzy systems. There are different types of neuro-fuzzy systems depending on whether the inputs, outputs, and weights are crisp or fuzzy. Two common models are fuzzy systems providing input to neural networks, and neural networks providing input to fuzzy systems. Neuro-fuzzy systems have applications in domains like measuring water opacity, improving financial ratings, and automatically adjusting devices.
Genetic algorithms and traditional algorithms differ in their definitions, usages, and complexity. Genetic algorithms are based on genetics and natural selection, and help find optimal solutions to difficult problems. They are more advanced than traditional algorithms which provide step-by-step procedures. Genetic algorithms are used in fields like machine learning and artificial intelligence, while traditional algorithms are used in programming and mathematics.
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
The document discusses VC dimension in machine learning. It introduces the concept of VC dimension as a measure of the capacity or complexity of a set of functions used in a statistical binary classification algorithm. VC dimension is defined as the largest number of points that can be shattered, or classified correctly, by the algorithm. The document notes that test error is related to both training error and model complexity, which can be measured by VC dimension. A low VC dimension or large training set size can help reduce the gap between training and test error.
Soft computing is an emerging approach to computing that aims to solve computationally hard problems using inexact solutions that are tolerant of imprecision, uncertainty, partial truth, and approximation. It uses techniques like fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning to model human-like decision making. Unlike hard computing which requires precise modeling and solutions, soft computing is well-suited for real-world problems where ideal models are not available. The key constituents of soft computing are fuzzy logic, evolutionary computation, neural networks, and machine learning.
Self-organizing networks can perform unsupervised clustering by mapping high-dimensional input patterns into a smaller number of clusters in output space through competitive learning. Fixed weight competitive networks like Maxnet, Mexican Hat net, and Hamming net use competitive learning with fixed weights. Maxnet uses winner-take-all competition to select the neuron whose weights best match the input. Mexican Hat net has both excitatory and inhibitory connections between neurons to enhance contrast. Hamming net determines which exemplar vector most closely matches the input using the Hamming distance measure.
This document outlines the syllabus for an MTCSCS302 course on Soft Computing taught by Dr. Sandeep Kumar Poonia. The course covers topics including neural networks, fuzzy logic, probabilistic reasoning, and genetic algorithms. It is divided into five units: (1) neural networks, (2) fuzzy logic, (3) fuzzy arithmetic and logic, (4) neuro-fuzzy systems and applications of fuzzy logic, and (5) genetic algorithms and their applications. The goal of the course is to provide students with knowledge of soft computing fundamentals and approaches for solving complex real-world problems.
T9. Trust and reputation in multi-agent systemsEASSS 2012
The credibility model in ReGreT evaluates the credibility of witnesses in two ways:
1. Direct trust in the witness - The trust that the agent has directly in the witness based on its past interactions. This is calculated using the direct trust model.
2. Reliability of the witness' reputation value - This measures how reliable or volatile the reputation values provided by the witness tend to be. It is calculated based on the number of outcomes the witness has observed and the deviation in its ratings.
The credibility model combines these two factors - direct trust and reliability - to get an overall credibility value for each witness. This credibility value is then used to weight the reputation values provided by each witness. Witnesses with higher credibility will have
This document discusses neural networks and fuzzy logic. It explains that neural networks can learn from data and feedback but are viewed as "black boxes", while fuzzy logic models are easier to comprehend but do not come with a learning algorithm. It then describes how neuro-fuzzy systems combine these two approaches by using neural networks to construct fuzzy rule-based models or fuzzy partitions of the input space. Specifically, it outlines the Adaptive Network-based Fuzzy Inference System (ANFIS) architecture, which is functionally equivalent to fuzzy inference systems and can represent both Sugeno and Tsukamoto fuzzy models using a five-layer feedforward neural network structure.
Reasoning is the process of deriving logical conclusions from facts or premises. There are several types of reasoning including deductive, inductive, abductive, analogical, and formal reasoning. Reasoning is a core component of artificial intelligence as AI systems must be able to reason about what they know to solve problems and draw new inferences. Formal logic provides the foundation for building reasoning systems through symbolic representations and inference rules.
The document discusses sources and approaches to handling uncertainty in artificial intelligence. It provides examples of uncertain inputs, knowledge, and outputs in AI systems. Common methods for representing and reasoning with uncertain data include probability, Bayesian belief networks, hidden Markov models, and temporal models. Effectively handling uncertainty through probability and inference allows AI to make rational decisions with imperfect knowledge.
Artificial Intelligence for Automated Decision Support ProjectValerii Klymchuk
Artificial intelligence can be used to develop automated decision support systems. There are different types of AI systems like expert systems, knowledge-based systems, and neural networks that can learn from data and apply rules to make decisions. One example is IBM's Watson, which uses natural language processing and evidence-based learning to provide personalized medical recommendations. Automated decision systems are rule-based and can make repetitive operational decisions in real-time, like pricing and loan approvals, freeing up human workers for more complex tasks. The key components of these systems are knowledge acquisition from experts, knowledge representation in a structured format like rules, and inference engines that apply the rules to draw new conclusions.
This Presentation discusses about the following topics:
Introduction to Intelligent Systems
Expert Systems
Neural Networks
Fuzzy Logic
Intelligent Agents
Expert Systems are computer programs that use knowledge and inference procedures to solve problems that normally require human expertise. They are designed to solve problems at an expert level by accessing a substantial knowledge base and applying reasoning mechanisms. Typical tasks for expert systems include data interpretation, diagnosis, structural analysis, planning, and prediction. Expert systems consist of a knowledge base, inference engine, user interface, knowledge acquisition system, and explanation facility. The inference engine applies rules and reasoning to the knowledge base to solve problems. Knowledge acquisition involves eliciting expertise from human experts to build the knowledge base.
This document presents a presentation on fuzzy expert systems. It introduces expert systems and how they combine human expertise with computational capabilities. It then discusses the evolution of fuzzy expert systems to handle imprecision and uncertainty. The key components of a fuzzy expert system are described, including the knowledge base, inference engine, and user interface. Steps for constructing a fuzzy expert system are outlined, from knowledge representation to testing rules. Pros and cons as well as applications in various domains like agriculture, education, and medicine are also summarized.
Fuzzy logic was introduced by Lotfi Zadeh in 1965 to address problems with classical logic being too precise. Fuzzy logic allows for truth values between 0 and 1 rather than binary true/false. It involves fuzzy sets, membership functions, linguistic variables, and fuzzy rules. Fuzzy logic can be applied to knowledge representation and inference using concepts like fuzzy predicates, relations, modifiers and quantifiers. It has various applications including household appliances, animation, industrial automation, and more.
Soft computing (ANN and Fuzzy Logic) : Dr. Purnima PanditPurnima Pandit
The document discusses soft computing and its techniques, including artificial neural networks (ANN). It provides an overview of ANN, including how biological neurons inspired the basic ANN model. A neuron has inputs, outputs, weights, and an activation function. Networks can be single or multilayer. Learning involves updating weights to minimize error, with backpropagation commonly used for multilayer networks. Applications include pattern recognition, function approximation, and parameter estimation. A simple example is provided to estimate the slope and intercept of a line using ANN.
This document discusses applications of soft computing techniques. It describes some key advantages of soft computing like modeling human reasoning and being comprehensible. It then outlines several applications of neural networks, fuzzy logic, and genetic algorithms in areas like control, business, finance, and others. Specific examples of soft computing applications covered in the chapter include combining multispectral and SAR images for flood analysis, using a genetic algorithm to optimize the traveling salesman problem, and developing soft computing based hybrid fuzzy controllers and rocket engine control systems.
This document provides an introduction to soft computing techniques including fuzzy logic, neural networks, and genetic algorithms. It discusses how these techniques are inspired by human intelligence and can handle imprecise or uncertain data. Examples of applications are given such as fuzzy logic in washing machines to optimize the washing process based on sensor readings, and using genetic algorithms to design optimal robotics.
Fuzzy inference systems use fuzzy logic to map inputs to outputs. There are two main types:
Mamdani systems use fuzzy outputs and are well-suited for problems involving human expert knowledge. Sugeno systems have faster computation using linear or constant outputs.
The fuzzy inference process involves fuzzifying inputs, applying fuzzy logic operators, and using if-then rules. Outputs are determined through implication, aggregation, and defuzzification. Mamdani systems find the centroid of fuzzy outputs while Sugeno uses weighted averages, making it more efficient.
This document provides an overview of fuzzy logic concepts for a course on soft computing. It discusses key fuzzy logic topics like membership functions, fuzzy sets, linguistic variables, fuzzy rules, fuzzy inference, and neuro-fuzzy systems. The document also provides examples of commonly used membership functions like triangular, trapezoidal, and Gaussian functions. It explains how fuzzy logic allows for approximate reasoning using natural language terms and multivalent logic with membership values between 0 and 1.
An knowledge based system (KBS) is a type of artificial intelligence program that uses a knowledge base to solve problems within a specialized domain that normally requires human expertise. A KBS consists of a knowledge base containing facts, rules, and heuristics about its domain, an inference engine that applies reasoning to the knowledge base, and a user interface. The knowledge base is developed by a knowledge engineer working with a domain expert to capture their expertise. A KBS can perform tasks like classification, diagnosis and planning by drawing on the captured knowledge through its inference engine.
Neuro-fuzzy systems combine neural networks and fuzzy logic to overcome the limitations of each. They were created to achieve the mapping precision of neural networks and the interpretability of fuzzy systems. There are different types of neuro-fuzzy systems depending on whether the inputs, outputs, and weights are crisp or fuzzy. Two common models are fuzzy systems providing input to neural networks, and neural networks providing input to fuzzy systems. Neuro-fuzzy systems have applications in domains like measuring water opacity, improving financial ratings, and automatically adjusting devices.
Genetic algorithms and traditional algorithms differ in their definitions, usages, and complexity. Genetic algorithms are based on genetics and natural selection, and help find optimal solutions to difficult problems. They are more advanced than traditional algorithms which provide step-by-step procedures. Genetic algorithms are used in fields like machine learning and artificial intelligence, while traditional algorithms are used in programming and mathematics.
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
The document discusses VC dimension in machine learning. It introduces the concept of VC dimension as a measure of the capacity or complexity of a set of functions used in a statistical binary classification algorithm. VC dimension is defined as the largest number of points that can be shattered, or classified correctly, by the algorithm. The document notes that test error is related to both training error and model complexity, which can be measured by VC dimension. A low VC dimension or large training set size can help reduce the gap between training and test error.
Soft computing is an emerging approach to computing that aims to solve computationally hard problems using inexact solutions that are tolerant of imprecision, uncertainty, partial truth, and approximation. It uses techniques like fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning to model human-like decision making. Unlike hard computing which requires precise modeling and solutions, soft computing is well-suited for real-world problems where ideal models are not available. The key constituents of soft computing are fuzzy logic, evolutionary computation, neural networks, and machine learning.
Self-organizing networks can perform unsupervised clustering by mapping high-dimensional input patterns into a smaller number of clusters in output space through competitive learning. Fixed weight competitive networks like Maxnet, Mexican Hat net, and Hamming net use competitive learning with fixed weights. Maxnet uses winner-take-all competition to select the neuron whose weights best match the input. Mexican Hat net has both excitatory and inhibitory connections between neurons to enhance contrast. Hamming net determines which exemplar vector most closely matches the input using the Hamming distance measure.
This document outlines the syllabus for an MTCSCS302 course on Soft Computing taught by Dr. Sandeep Kumar Poonia. The course covers topics including neural networks, fuzzy logic, probabilistic reasoning, and genetic algorithms. It is divided into five units: (1) neural networks, (2) fuzzy logic, (3) fuzzy arithmetic and logic, (4) neuro-fuzzy systems and applications of fuzzy logic, and (5) genetic algorithms and their applications. The goal of the course is to provide students with knowledge of soft computing fundamentals and approaches for solving complex real-world problems.
T9. Trust and reputation in multi-agent systemsEASSS 2012
The credibility model in ReGreT evaluates the credibility of witnesses in two ways:
1. Direct trust in the witness - The trust that the agent has directly in the witness based on its past interactions. This is calculated using the direct trust model.
2. Reliability of the witness' reputation value - This measures how reliable or volatile the reputation values provided by the witness tend to be. It is calculated based on the number of outcomes the witness has observed and the deviation in its ratings.
The credibility model combines these two factors - direct trust and reliability - to get an overall credibility value for each witness. This credibility value is then used to weight the reputation values provided by each witness. Witnesses with higher credibility will have
This document discusses neural networks and fuzzy logic. It explains that neural networks can learn from data and feedback but are viewed as "black boxes", while fuzzy logic models are easier to comprehend but do not come with a learning algorithm. It then describes how neuro-fuzzy systems combine these two approaches by using neural networks to construct fuzzy rule-based models or fuzzy partitions of the input space. Specifically, it outlines the Adaptive Network-based Fuzzy Inference System (ANFIS) architecture, which is functionally equivalent to fuzzy inference systems and can represent both Sugeno and Tsukamoto fuzzy models using a five-layer feedforward neural network structure.
Reasoning is the process of deriving logical conclusions from facts or premises. There are several types of reasoning including deductive, inductive, abductive, analogical, and formal reasoning. Reasoning is a core component of artificial intelligence as AI systems must be able to reason about what they know to solve problems and draw new inferences. Formal logic provides the foundation for building reasoning systems through symbolic representations and inference rules.
The document discusses sources and approaches to handling uncertainty in artificial intelligence. It provides examples of uncertain inputs, knowledge, and outputs in AI systems. Common methods for representing and reasoning with uncertain data include probability, Bayesian belief networks, hidden Markov models, and temporal models. Effectively handling uncertainty through probability and inference allows AI to make rational decisions with imperfect knowledge.
Artificial Intelligence for Automated Decision Support ProjectValerii Klymchuk
Artificial intelligence can be used to develop automated decision support systems. There are different types of AI systems like expert systems, knowledge-based systems, and neural networks that can learn from data and apply rules to make decisions. One example is IBM's Watson, which uses natural language processing and evidence-based learning to provide personalized medical recommendations. Automated decision systems are rule-based and can make repetitive operational decisions in real-time, like pricing and loan approvals, freeing up human workers for more complex tasks. The key components of these systems are knowledge acquisition from experts, knowledge representation in a structured format like rules, and inference engines that apply the rules to draw new conclusions.
This Presentation discusses about the following topics:
Introduction to Intelligent Systems
Expert Systems
Neural Networks
Fuzzy Logic
Intelligent Agents
Following topics are discussed in this presentation:What is Soft Computing?
What is Hard Computing?
What is Fuzzy Logic Models?
What is Neural Networks (NN)?
What is Genetic Algorithms or Evaluation Programming?
What is probabilistic reasoning?
Difference between fuzziness and probability
AI and Soft Computing
Future of Soft Computing
Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
The document provides an introduction to expert systems. It defines an expert system as a computer system that emulates the decision-making abilities of a human expert. The key components of an expert system are a knowledge base containing the expertise knowledge and an inference engine that draws conclusions from the knowledge base. Expert systems offer advantages like increased availability, reduced costs, reliability, and the ability to explain their reasoning. However, they also have limitations like dealing with uncertainty and an inability to generalize knowledge like humans.
This chapter introduces expert systems and their key components. It discusses that an expert system emulates the decision-making abilities of a human expert. The chapter outlines the objectives of understanding expert systems' problem domains, knowledge domains, development stages, and applications. It also examines the components of rule-based expert systems, including their knowledge bases, inference engines, rules, and inference cycles. The chapter compares procedural and nonprocedural programming paradigms as well as different types of expert system languages.
This chapter introduces expert systems and their key components. It discusses that an expert system emulates the decision-making abilities of a human expert. The chapter outlines the objectives of understanding expert systems' problem domains, knowledge domains, development stages, and applications. It also examines the components of rule-based expert systems, including their knowledge bases, inference engines, rules, and inference cycles. The chapter compares procedural and nonprocedural programming paradigms as well as different types of expert system languages.
The first lecture of expert system with python course.
Enjoy!
you can find the second lecture here:
https://www.slideshare.net/ahmadhussein45/expert-system-with-python-2
Artificial intelligence in power systemBittu Goswami
This document discusses the use of artificial intelligence techniques like expert systems, artificial neural networks, and fuzzy logic in power systems. It provides an overview of each technique, their advantages and disadvantages, and examples of how they can be applied. Specifically, it describes how expert systems can be used for transmission line parameter estimation, and how neural networks and fuzzy logic can be applied to fault detection and diagnosis to improve system reliability and efficiency. The document concludes that while AI is increasingly being used in power systems, further research is still needed to fully realize its benefits.
An expert system aims to emulate human expertise to solve problems that normally require human experts. It consists of a knowledge base that stores facts and rules, an inference engine that applies reasoning to derive solutions, and a user interface for interaction. Expert systems are useful when human experts are unavailable or in high demand. They capture specialist knowledge in domains like medicine, engineering, and oil exploration to make it more accessible.
This presentation discusses about the following topics:
Truth values and tables,
Fuzzy propositions,
Formation of rules decomposition of rules,
Aggregation of fuzzy rules,
Fuzzy reasoning‐fuzzy inference systems
Overview of fuzzy expert system‐
Fuzzy decision making.
An expert system uses artificial intelligence to simulate the decision-making of a human expert. It contains a knowledge base of rules and facts, an inference engine that reasons about the knowledge, and a user interface. The knowledge base contains declarative and procedural knowledge in a rule-based format. The inference engine derives answers and the user interface allows communication. There are five stages to developing an expert system: identification, conceptualization, formalization, implementation, and testing.
Computer Aided Development of Fuzzy, Neural and Neuro-Fuzzy SystemsIJEACS
Development of an expert system is difficult because of two challenges involve in it. The first one is the expert system itself is high level system and deals with knowledge, which make is difficult to handle. Second, the systems development is more art and less science; hence there are little guidelines available about the development. This paper describes computer aided development of intelligent systems using modem artificial intelligence technology. The paper illustrates a design of a reusable generic framework to support friendly development of fuzzy, neural network and hybrid systems such as neuro-fuzzy system. The reusable component libraries for fuzzy logic based systems, neural network based system and hybrid system such as neuro-fuzzy system are developed and accommodated in this framework. The paper demonstrates code snippets, interface screens and class libraries overview with necessary technical details.
This document introduces soft computing and provides an agenda for the lecture. Soft computing is defined as a fusion of fuzzy logic, neural networks, evolutionary computing, and probabilistic computing to deal with uncertainty and imprecision. Hybrid systems combine different soft computing techniques for improved performance. The lecture will cover an introduction to soft computing, fuzzy computing, neural networks, evolutionary computing, and hybrid systems. References are also provided.
A Review on Reasoning System, Types, and Tools and Need for Hybrid ReasoningBRNSSPublicationHubI
This document summarizes a review article about reasoning systems, types of reasoning, and the need for hybrid reasoning systems. It discusses expert systems and how they use knowledge representation and reasoning to emulate expert decision making. The main types of reasoning discussed are deductive, inductive, and abductive reasoning. It also introduces the concept of a hybrid reasoning system that integrates two different types of reasoning to provide both qualitative and quantitative assessments.
AI neural networks can support disaster recovery and security operations in cloud computing systems. A neural network model is proposed that monitors a cloud computing network and can rebuild failed systems through new neural connections. The network uses cooperative coevolution algorithms and evolutionary algorithms to automate remediation. It involves distributed problem solving agents across the cloud network and a layered neural network collective that independently evaluates needs and repairs. This provides a robust, self-healing organizational model for cloud computing infrastructure and operations.
Ali Akram Saber's document discusses intelligent urban traffic control systems using various artificial intelligence techniques. It covers neural networks, genetic algorithms, expert systems, fuzzy logic, and rule-based systems. Neural networks can be separated into models, networks, and learning rules. Genetic algorithms mimic natural selection to find solutions. Expert systems contain knowledge bases and reasoning engines. Rule-based systems separate knowledge from execution. Fuzzy logic handles approximate reasoning between true and false values.
The document discusses expert systems, which are computer systems that emulate the decision-making ability of a human expert. It describes the typical architecture of an expert system, which includes a knowledge base, inference engine, user interface, explanation facility, and knowledge acquisition system. It provides details on key components like the knowledge base, which stores rules and data, and the inference engine, which applies rules and reasoning to derive conclusions. Specific expert systems are discussed like MYCIN for medical diagnosis, DART for computer fault diagnosis, and XCON for configuring DEC computer systems. The roles of knowledge engineers and domain experts in developing expert systems are also outlined.
This document summarizes and evaluates various rule extraction algorithms from trained artificial neural networks. It begins with an introduction explaining the importance of explanation capabilities for neural networks. It then provides a taxonomy for classifying rule extraction approaches based on the expressiveness of the extracted rules, whether the approach takes an open-box or black-box view of the neural network, any specialized training regimes used, the quality of explanations generated, and computational complexity. The document discusses sensitivity analysis as a basic method for understanding neural network relationships before focusing on decompositional and pedagogical rule extraction approaches.
This document provides an overview of soft computing techniques including neural networks, fuzzy logic, genetic algorithms, and hybrid systems. It discusses how neural networks are inspired by the human brain and can learn from examples to perform tasks like object recognition. Fuzzy logic allows for partial membership in sets and handles imprecise data. Genetic algorithms use selection, crossover and mutation to evolve solutions to problems. Hybrid systems combine techniques, such as neurofuzzy and neurogenetic systems. Soft computing is used to solve complex problems with approximate models, unlike hard computing which uses precise models.
IRJET- Factoid Question and Answering SystemIRJET Journal
This document describes a factoid question answering system that uses neural networks and the Tensorflow framework. The system takes in a text document and question as input. It then processes the input using techniques like gated recurrent units and support vector machines to classify the question. The system calculates attention between facts and the question, modifies its memory, and identifies the word closest to the answer to output as the response. Key aspects of the system include training a question answering engine with Tensorflow, storing and retrieving data, and generating the final answer.
This presentation discusses the following topics:
Basic features of R
Exploring R GUI
Data Frames & Lists
Handling Data in R Workspace
Reading Data Sets & Exporting Data from R
Manipulating & Processing Data in R
Association rule mining is used to find relationships between items in transaction data. It identifies rules that can predict the occurrence of an item based on other items purchased together frequently. Some key metrics used to evaluate rules include support, which measures how frequently an itemset occurs; confidence, which measures how often items in the predicted set occur given items in the predictor set; and lift, which compares the confidence to expected confidence if items were independent. An example association rule evaluated is {Milk, Diaper} -> {Beer} with support of 0.4, confidence of 0.67, and lift of 1.11.
This document discusses clustering, which is the task of grouping data points into clusters so that points within the same cluster are more similar to each other than points in other clusters. It describes different types of clustering methods, including density-based, hierarchical, partitioning, and grid-based methods. It provides examples of specific clustering algorithms like K-means, DBSCAN, and discusses applications of clustering in fields like marketing, biology, libraries, insurance, city planning, and earthquake studies.
Classification is a data analysis technique used to predict class membership for new observations based on a training set of previously labeled examples. It involves building a classification model during a training phase using an algorithm, then testing the model on new data to estimate accuracy. Some common classification algorithms include decision trees, Bayesian networks, neural networks, and support vector machines. Classification has applications in domains like medicine, retail, and entertainment.
The document discusses the assumptions and properties of ordinary least squares (OLS) estimators in linear regression analysis. It notes that OLS estimators are best linear unbiased estimators (BLUE) if the assumptions of the linear regression model are met. Specifically, it assumes errors have zero mean and constant variance, are uncorrelated, and are normally distributed. Violation of the assumption of constant variance is known as heteroscedasticity. The document outlines how heteroscedasticity impacts the properties of OLS estimators and their use in applications like econometrics.
This document provides an introduction to regression analysis. It discusses that regression analysis investigates the relationship between dependent and independent variables to model and analyze data. The document outlines different types of regressions including linear, polynomial, stepwise, ridge, lasso, and elastic net regressions. It explains that regression analysis is used for predictive modeling, forecasting, and determining the impact of variables. The benefits of regression analysis are that it indicates significant relationships and the strength of impact between variables.
MYCIN was an early expert system developed at Stanford University in 1972 to assist physicians in diagnosing and selecting treatment for bacterial and blood infections. It used over 600 production rules encoding the clinical decision criteria of infectious disease experts to diagnose patients based on reported symptoms and test results. While it could not replace human diagnosis due to computing limitations at the time, MYCIN demonstrated that expert knowledge could be represented computationally and established a foundation for more advanced machine learning and knowledge base systems.
The document discusses expert systems, which are computer applications that solve complex problems at a human expert level. It describes the characteristics and capabilities of expert systems, why they are useful, and their key components - knowledge base, inference engine, and user interface. The document also outlines common applications of expert systems and the general development process.
The Dempster-Shafer Theory was developed by Arthur Dempster in 1967 and Glenn Shafer in 1976 as an alternative to Bayesian probability. It allows one to combine evidence from different sources and obtain a degree of belief (or probability) for some event. The theory uses belief functions and plausibility functions to represent degrees of belief for various hypotheses given certain evidence. It was developed to describe ignorance and consider all possible outcomes, unlike Bayesian probability which only considers single evidence. An example is given of using the theory to determine the murderer in a room with 4 people where the lights went out.
A Bayesian network is a probabilistic graphical model that represents conditional dependencies among random variables using a directed acyclic graph. It consists of nodes representing variables and directed edges representing causal relationships. Each node contains a conditional probability table that quantifies the effect of its parent nodes on that variable. Bayesian networks can be used to calculate the probability of events occurring based on the network structure and conditional probability tables, such as computing the probability of an alarm sounding given that no burglary or earthquake occurred but two neighbors called.
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.
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.
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.
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.
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.
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”Dr. C.V. Suresh Babu
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”Dr. C.V. Suresh Babu
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.
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Chapter 4 - Islamic Financial Institutions in Malaysia.pptx
Hybrid systems
1. Department of Information Technology 1Soft Computing (ITC4256 )
Dr. C.V. Suresh Babu
Professor
Department of IT
Hindustan Institute of Science & Technology
Hybrid Systems
2. Department of Information Technology 2Soft Computing (ITC4256 )
Action Plan
• Hybrid Systems
• Hybridization
• Combinations
• Comparison of Expert Systems, Fuzzy Systems, Neural Networks and Genetic Algorithms
• Current Progress
• Primary Components
• MultiComponents
• Degree of Integration
• Transformational, hierarchial and integrated
• Stand Alone Models
• Integrated – Fused Architectures
• Generalized Fused Framework
• System Types for Hybridization
• Quiz
3. Department of Information Technology 3Soft Computing (ITC4256 )
A hybrid intelligent system is one that combines at least two intelligent technologies.
For example, combining a neural network with a fuzzy system results in a hybrid neuro-fuzzy
system.
The combination of:
probabilistic reasoning,
fuzzy logic,
neural networks and
evolutionary computation forms the core of soft computing,
Soft Computing is an emerging approach to building hybrid intelligent systems capable of reasoning
and learning in an uncertain and imprecise environment.
Hybrid Systems
4. Department of Information Technology 4Soft Computing (ITC4256 )
Hybridization
• Integrated architectures for machine learning have been shown
to provide performance improvements over single
representation architectures.
• Integration, or hybridization, is achieved using a spectrum of
module or component architectures ranging from those sharing
independently functioning components to architectures in which
different components are combined in inherently inseparable
ways.
• In this presentation we briefly survey prototypical integrated
architectures
5. Department of Information Technology 5Soft Computing (ITC4256 )
Although words are less precise than numbers, precision carries a high cost.
We use words when there is a tolerance for imprecision.
Soft computing exploits the tolerance for uncertainty and imprecision to
achieve greater tractability and robustness, and lower the cost of solutions.
We also use words when the available data is not precise enough to use
numbers.
This is often the case with complex problems, and while “hard” computing
fails to produce any solution, soft computing is still capable of finding good
solutions.
Using “words” rather than strict numbers
6. Department of Information Technology 6Soft Computing (ITC4256 )
Lotfi Zadeh is reputed to have said that a good hybrid would be
“British Police, German Mechanics, French Cuisine, Swiss Banking
and Italian Love”.
But “British Cuisine, German Police, French Mechanics, Italian
Banking and Swiss Love” would be a bad one.
Likewise, a hybrid intelligent system can be good or bad – it depends
on which components constitute the hybrid.
So our goal is to select the right components for building a good
hybrid system.
7. Department of Information Technology 7Soft Computing (ITC4256 )
Comparison of Expert Systems, Fuzzy Systems,
Neural Networks and Genetic Algorithms
Knowledge representation
Uncertainty tolerance
Imprecision tolerance
Adaptability
Learning ability
Explanation ability
Knowledge discovery and data mining
Maintainability
ES FS NN GA
* The terms used for grading are:
- bad, - rather bad, - good - rather good and
8. Department of Information Technology 8Soft Computing (ITC4256 )
The combination of knowledge based systems, neural networks and
evolutionary computation forms the core of an emerging approach to
building hybrid intelligent systems capable of reasoning and learning
in an uncertain and imprecise environment.
Combinations
9. Department of Information Technology 9Soft Computing (ITC4256 )
Current Progress
• In recent years multiple module integrated machine learning
systems have been developed to overcome the limitations inherent
in single component systems.
• Integrations of neural networks (NN), fuzzy logic (FL) and global
optimization algorithms have received considerable attention but
increasing attention is being paid to integrations with case based
reasoning (CBR) and rule induction (RI).
10. Department of Information Technology 10Soft Computing (ITC4256 )
Primary Components
• The full spectrum of knowledge representation in such systems is
not confined to the primary components.
• For example, in CBR systems although much knowledge resides in
the case library significant problem solving knowledge may reside in
secondary technologies such as in the similarity metric used to
retrieve problem solution pairs from the case library, in the
adaptation mechanisms used to improve an approximate solution
and in the case library maintenance mechanisms.
11. Department of Information Technology 11Soft Computing (ITC4256 )
MultiComponents
• Although it is possible to generalize about the relative utilities of
these component types based on the primary knowledge
representation mechanisms these generalizations may no longer
remain valid in particular cases depending on the characteristics of
the secondary mechanisms employed.
• Table 1 attempts to gauge the relative utilities of single components
systems based on the primary knowledge representation.
12. Department of Information Technology 12Soft Computing (ITC4256 )
Degree of Integration
• Besides differing in the types of component systems employed, different
integrated architectures have emerged in a rather ad hoc way.
• Least integrated architectures consisting of independent components
communicating with each other on a side by side basis.
• More integration is shown in transformational or hierarchial systems in which
one technique may be used for development and another for delivery or one
component may be used to optimize the performance of another component.
• More fully integrated architectures combine different effects to produce a
balanced overall computational model.
13. Department of Information Technology 13Soft Computing (ITC4256 )
Transformational,
hierarchial and integrated
• This categorizeses such systems as transformational,
hierarchial and integrated. In a transformational integrated
system the system may use one type of component to produce
another which is the functional system.
• For example, a rule based system may be used to set the
initial conditions for a neural network solution to a problem.
• Thus, to create a modern intelligent system it may be
necessary to make a choice of complementary techniques.
14. Department of Information Technology 14Soft Computing (ITC4256 )
Stand Alone Models
• Independent components that do not interact
• Solving problems that have naturally independent
components – eg., decision support and categorization
15. Department of Information Technology 15Soft Computing (ITC4256 )
Transformational
• Expert systems with neural networks
• Knowledge from the ES is used to set the initial conditions
and training set of the NN
16. Department of Information Technology 16Soft Computing (ITC4256 )
Hierarchial Hybrid
• An ANN uses a GA to optimize its topology and the
output fed into an ES which creates the desired output
or explanation
17. Department of Information Technology 17Soft Computing (ITC4256 )
Integrated – Fused Architectures
• Combine different techniques in one computational
model
• Share data structures and knowledge representations
• Extended range of capabilities – e.g., classification with
explanation, or, adaptation with classification
19. Department of Information Technology 19Soft Computing (ITC4256 )
Fused Architecture
The architecture consists of four components and the environment.
• The performance element (PE) is the actual controller.
• The learning element.(LE) updates the knowledge in the PE .
The LE has access to the environment, the past states and the performance
measure. It updates the PE. The examines the external performance
and provides feedback to the LE. The critic faces the problem of converting
an external reinforcement into an internal one. The problem generator is to
contribute to the exploration of the problem space in an efficient way.
The framework does not specify the techniques.
20. Department of Information Technology 20Soft Computing (ITC4256 )
System Types for Hybridization
• Knowledge-based Systems and if-then rules
• CBR Systems
• Evolutionary Intelligence and Genetic algorithms
• Artificial Neural Networks and Learning
• Fuzzy Systems
• PSO Systems
21. Department of Information Technology 21Soft Computing (ITC4256 )
Knowledge in Intelligent Systems
• In rule induction systems knowledge is represented explicitly by if-then rules
that are obtained from example sets.
• In neural networks knowledge is captures in synaptic weights in systems of
neurons that capture categorizations in data sets.
• In evolutionary systems knowledge is captured in evolving pools of selected
genes and in heuristics for selection of more adapted chromosomes.
• In case based systems knowledge is primarily stored in the form of case
histories that represent previously developed problem-solution pairs.
• In PSO systems the knowledge is stored in the prticle swarms
22. Department of Information Technology 22Soft Computing (ITC4256 )
CBR KB NN GA FL
Know. rep. 3 4 1 2 4
Uncertainty 1 1 4 4 4
Approximation (noisy
incomplete data)
1 1 4 4 4
Adaptable 4 2 4 4 2
Learnable 3 1 4 4 2
Interpretable 3 4 1 2 4
Table 1 (Adapted from [Abr, Jac] and [Neg]). A comparison of the utility of
case based reasoning systems (CBR), rule induction systems (RI),
neural networks (NN) genetic algorithms (GA) and fuzzy systems (FS),
with 1 representing low and 4 representing a high utility.
23. Department of Information Technology 23Soft Computing (ITC4256 )
Interpretability
• Synaptic weights in trained neural networks are not easy to
interpret with particular difficulties if interpretations are required.
• Genetic algorithms model natural genetic adaptation to changing
environments and thus are inherently adaptable and learn well
• Not easily interpretable because although the knowledge resides
partly in the selection mechanism it is in the most part deeply
embedded within a population of adapted genes.
24. Department of Information Technology 24Soft Computing (ITC4256 )
Adaptability
• Case based systems are adaptable because changing
the case library may be sufficient to port a system to a
related area. If changes need to be made to the similarity
metric or the adaptation mechanism or if the case
structure needs to be changed much more work may be
required.
25. Department of Information Technology 25Soft Computing (ITC4256 )
Learnability
• Fuzzy rule based systems offer more option through
which learnability may be more easily achieved.
• Fuzzy rules may be fine tuned by adjusting the shapes of
the fuzzy sets according to user feedback
26. Department of Information Technology 26Soft Computing (ITC4256 )
Rules and cases
• Rule based systems employ an easily comprehensible but rigid
representation of expert knowledge such systems may afford
better interpretation mechanisms.
• Similarly recent research shows [SØR] that explanation
techniques for large case bases is most promising while case
based learning and maintenance can often be very efficient
because of the transparency of typical case libraries.
27. Department of Information Technology 27Soft Computing (ITC4256 )
Test Yourself
1. When it comes to the areas of data and knowledge, computers are much better at handling:
A. knowledge first, then processing the data.
B. knowledge than data.
C. data than knowledge.
D. only knowledge.
2. When a computer can correctly recognize faces of users with a high degree of reliability, it is using:
A. fuzzy logic.
B. pattern recognition.
C. image analysis.
D. OCR.
3. A software program designed to replicate the decision-making process of a human expert is a(n):
A. data system.
B. database.
C. expert system.
D. semantic system.
4. When a conclusion is stated as a probability rather than an exact fact, it is known as:
A. an expert system.
B. a database.
C. fuzzy logic.
D. a pattern recognition system
5. Expert systems primarily started in the:
A. insurance field.
B. medical field.
C. aviation field.
D. library reference field.
28. Department of Information Technology 28Soft Computing (ITC4256 )
Answers
1. When it comes to the areas of data and knowledge, computers are much better at handling:
A. knowledge first, then processing the data.
B. knowledge than data.
C. data than knowledge.
D. only knowledge.
2. When a computer can correctly recognize faces of users with a high degree of reliability, it is using:
A. fuzzy logic.
B. pattern recognition.
C. image analysis.
D. OCR.
3. A software program designed to replicate the decision-making process of a human expert is a(n):
A. data system.
B. database.
C. expert system.
D. semantic system.
4. When a conclusion is stated as a probability rather than an exact fact, it is known as:
A. an expert system.
B. a database.
C. fuzzy logic.
D. a pattern recognition system
5. Expert systems primarily started in the:
A. insurance field.
B. medical field.
C. aviation field.
D. library reference field.