The document discusses expert systems in artificial intelligence. It describes what an expert system is and its key components, including the knowledge base, inference engine, and user interface. The document provides examples of various expert systems such as MYCIN, DENDRAL, and Watson. It also discusses probability-based expert systems and provides an example of a medical diagnosis expert system.
This document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem or calculate a quantity. Algorithm analysis involves evaluating memory usage and time complexity. Asymptotics, such as Big-O notation, are used to formalize the growth rates of algorithms. Common sorting algorithms like insertion sort and quicksort are analyzed using recurrence relations to determine their time complexities as O(n^2) and O(nlogn), respectively.
Dendral was an early expert system developed in the 1960s at Stanford University to help organic chemists identify unknown organic molecules. It used mass spectrometry data and knowledge of chemistry to generate possible chemical structures. Dendral included both Heuristic Dendral, which produced candidate structures, and Meta Dendral, a machine learning system that proposed mass spectrometry rules relating structure to spectra. The project pioneered the use of heuristics programming and helped establish artificial intelligence approaches like the plan-generate-test problem solving paradigm. Many subsequent expert systems were influenced by Dendral.
This document discusses Bayesian learning and the Bayes theorem. Some key points:
- Bayesian learning uses probabilities to calculate the likelihood of hypotheses given observed data and prior probabilities. The naive Bayes classifier is an example.
- The Bayes theorem provides a way to calculate the posterior probability of a hypothesis given observed training data by considering the prior probability and likelihood of the data under the hypothesis.
- Bayesian methods can incorporate prior knowledge and probabilistic predictions, and classify new instances by combining predictions from multiple hypotheses weighted by their probabilities.
The document discusses key concepts in machine learning theory such as sample complexity, computational complexity, and mistake bounds. It focuses on analyzing the performance of broad classes of learning algorithms characterized by their hypothesis space. Specific topics covered include probably approximately correct (PAC) learning, sample complexity for finite vs infinite hypothesis spaces, and mistake bounds for algorithms like HALVING and weighted majority. The goal is to understand how many training examples and computational steps are needed for a learner to converge to a successful hypothesis.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
Lazy learning is a machine learning method where generalization of training data is delayed until a query is made, unlike eager learning which generalizes before queries. K-nearest neighbors and case-based reasoning are examples of lazy learners, which store training data and classify new data based on similarity. Case-based reasoning specifically stores prior problem solutions to solve new problems by combining similar past case solutions.
This document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem or calculate a quantity. Algorithm analysis involves evaluating memory usage and time complexity. Asymptotics, such as Big-O notation, are used to formalize the growth rates of algorithms. Common sorting algorithms like insertion sort and quicksort are analyzed using recurrence relations to determine their time complexities as O(n^2) and O(nlogn), respectively.
Dendral was an early expert system developed in the 1960s at Stanford University to help organic chemists identify unknown organic molecules. It used mass spectrometry data and knowledge of chemistry to generate possible chemical structures. Dendral included both Heuristic Dendral, which produced candidate structures, and Meta Dendral, a machine learning system that proposed mass spectrometry rules relating structure to spectra. The project pioneered the use of heuristics programming and helped establish artificial intelligence approaches like the plan-generate-test problem solving paradigm. Many subsequent expert systems were influenced by Dendral.
This document discusses Bayesian learning and the Bayes theorem. Some key points:
- Bayesian learning uses probabilities to calculate the likelihood of hypotheses given observed data and prior probabilities. The naive Bayes classifier is an example.
- The Bayes theorem provides a way to calculate the posterior probability of a hypothesis given observed training data by considering the prior probability and likelihood of the data under the hypothesis.
- Bayesian methods can incorporate prior knowledge and probabilistic predictions, and classify new instances by combining predictions from multiple hypotheses weighted by their probabilities.
The document discusses key concepts in machine learning theory such as sample complexity, computational complexity, and mistake bounds. It focuses on analyzing the performance of broad classes of learning algorithms characterized by their hypothesis space. Specific topics covered include probably approximately correct (PAC) learning, sample complexity for finite vs infinite hypothesis spaces, and mistake bounds for algorithms like HALVING and weighted majority. The goal is to understand how many training examples and computational steps are needed for a learner to converge to a successful hypothesis.
Classification techniques in data miningKamal Acharya
The document discusses classification algorithms in machine learning. It provides an overview of various classification algorithms including decision tree classifiers, rule-based classifiers, nearest neighbor classifiers, Bayesian classifiers, and artificial neural network classifiers. It then describes the supervised learning process for classification, which involves using a training set to construct a classification model and then applying the model to a test set to classify new data. Finally, it provides a detailed example of how a decision tree classifier is constructed from a training dataset and how it can be used to classify data in the test set.
Lazy learning is a machine learning method where generalization of training data is delayed until a query is made, unlike eager learning which generalizes before queries. K-nearest neighbors and case-based reasoning are examples of lazy learners, which store training data and classify new data based on similarity. Case-based reasoning specifically stores prior problem solutions to solve new problems by combining similar past case solutions.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
This document discusses handling uncertainty through probabilistic reasoning and machine learning techniques. It covers sources of uncertainty like incomplete data, probabilistic effects, and uncertain outputs from inference. Approaches covered include Bayesian networks, Bayes' theorem, conditional probability, joint probability distributions, and Dempster-Shafer theory. It provides examples of calculating conditional probabilities and using Bayes' theorem. Bayesian networks are defined as directed acyclic graphs representing probabilistic dependencies between variables, and examples show how to represent domains of uncertainty and perform probabilistic reasoning using a Bayesian network.
Fuzzy ARTMAP is a neural network architecture that uses fuzzy logic and adaptive resonance theory (ART) for supervised learning. It incorporates two fuzzy ART modules, ART-a and ART-b, linked together by an inter-ART module called the MAP field. This allows the network to form predictive associations between categories and track matches using a mechanism called match tracking. The match tracking recognizes category structures to avoid repeating predictive errors on subsequent inputs. Fuzzy ARTMAP is trained until it can correctly classify all training data by increasing the vigilance parameter of ART-a in response to predictive mismatches at ART-b.
This document provides an overview of database system concepts and architecture. It discusses data models, schemas, instances, and states. It also describes the three-schema architecture, data independence, DBMS languages and interfaces, database system utilities and tools, and centralized and client-server architectures. Key classification of DBMSs are also covered.
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.
This document provides an overview of Naive Bayes classification. It begins with background on classification methods, then covers Bayes' theorem and how it relates to Bayesian and maximum likelihood classification. The document introduces Naive Bayes classification, which makes a strong independence assumption to simplify probability calculations. It discusses algorithms for discrete and continuous features, and addresses common issues like dealing with zero probabilities. The document concludes by outlining some applications of Naive Bayes classification and its advantages of simplicity and effectiveness for many problems.
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 document discusses and provides examples of supervised and unsupervised learning. Supervised learning involves using labeled training data to learn relationships between inputs and outputs and make predictions. An example is using data on patients' attributes to predict the likelihood of a heart attack. Unsupervised learning involves discovering hidden patterns in unlabeled data by grouping or clustering items with similar attributes, like grouping fruits by color without labels. The goal of supervised learning is to build models that can make predictions when new examples are presented.
An overview of gradient descent optimization algorithms Hakky St
This document provides an overview of various gradient descent optimization algorithms that are commonly used for training deep learning models. It begins with an introduction to gradient descent and its variants, including batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent. It then discusses challenges with these algorithms, such as choosing the learning rate. The document proceeds to explain popular optimization algorithms used to address these challenges, including momentum, Nesterov accelerated gradient, Adagrad, Adadelta, RMSprop, and Adam. It provides visualizations and intuitive explanations of how these algorithms work. Finally, it discusses strategies for parallelizing and optimizing SGD and concludes with a comparison of optimization algorithms.
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.
-BayesianLearning in machine Learning 12Kumari Naveen
1. Bayesian learning methods are relevant to machine learning for two reasons: they provide practical classification algorithms like naive Bayes, and provide a useful perspective for understanding many learning algorithms.
2. Bayesian learning allows combining observed data with prior knowledge to determine the probability of hypotheses. It provides optimal decision making and can accommodate probabilistic predictions.
3. While Bayesian methods may require estimating probabilities and have high computational costs, they provide a standard for measuring other practical methods.
1. A perceptron is a basic artificial neural network that can learn linearly separable patterns. It takes weighted inputs, applies an activation function, and outputs a single binary value.
2. Multilayer perceptrons can learn non-linear patterns by using multiple layers of perceptrons with weighted connections between them. They were developed to overcome limitations of single-layer perceptrons.
3. Perceptrons are trained using an error-correction learning rule called the delta rule or the least mean squares algorithm. Weights are adjusted to minimize the error between the actual and target outputs.
The document discusses data preprocessing techniques. It explains that data preprocessing is important because real-world data is often noisy, incomplete, and inconsistent. The key techniques covered are data cleaning, integration, reduction, and transformation. Data cleaning handles missing values, noise, and outliers. Data integration merges data from multiple sources. Data reduction reduces data size through techniques like dimensionality reduction. Data transformation normalizes and aggregates data to make it suitable for mining.
The document discusses gradient descent methods for unconstrained convex optimization problems. It introduces gradient descent as an iterative method to find the minimum of a differentiable function by taking steps proportional to the negative gradient. It describes the basic gradient descent update rule and discusses convergence conditions such as Lipschitz continuity, strong convexity, and condition number. It also covers techniques like exact line search, backtracking line search, coordinate descent, and steepest descent methods.
This document discusses machine learning concepts like supervised and unsupervised learning. It explains that supervised learning uses known inputs and outputs to learn rules while unsupervised learning deals with unknown inputs and outputs. Classification and regression are described as types of supervised learning problems. Classification involves categorizing data into classes while regression predicts continuous, real-valued outputs. Examples of classification and regression problems are provided. Classification models like heuristic, separation, regression and probabilistic models are also mentioned. The document encourages learning more about classification algorithms in upcoming videos.
The document discusses machine learning algorithms for learning first-order logic rules from examples. It introduces the FOIL algorithm, which extends propositional rule learning algorithms like sequential covering to learn more general first-order Horn clauses. FOIL uses an iterative specialization approach, starting with a general rule and greedily adding literals to better fit examples while avoiding negative examples. The document also discusses how combining inductive learning with analytical domain knowledge can improve learning, such as in the KBANN approach where a neural network is initialized based on the domain theory before training on examples.
The word ‘stochastic‘ means a system or process linked with a random probability. Hence, in Stochastic Gradient Descent, a few samples are selected randomly instead of the whole data set for each iteration. In Gradient Descent, there is a term called “batch” which denotes the total number of samples from a dataset that is used for calculating the gradient for each iteration. In typical Gradient Descent optimization, like Batch Gradient Descent, the batch is taken to be the whole dataset. Although using the whole dataset is really useful for getting to the minima in a less noisy and less random manner, the problem arises when our dataset gets big.
Suppose, you have a million samples in your dataset, so if you use a typical Gradient Descent optimization technique, you will have to use all of the one million samples for completing one iteration while performing the Gradient Descent, and it has to be done for every iteration until the minima are reached. Hence, it becomes computationally very expensive to perform.
This problem is solved by Stochastic Gradient Descent. In SGD, it uses only a single sample, i.e., a batch size of one, to perform each iteration. The sample is randomly shuffled and selected for performing the iteration.
Expert System in Artificial Intelligences7118080008
An expert system is a computer program designed to solve complex problems like a human expert. It uses knowledge stored in its knowledge base and reasoning rules to determine solutions. The first expert system was developed in 1970. It consists of a user interface, inference engine, and knowledge base. The inference engine applies rules to the knowledge base to derive conclusions. Popular examples include DENDRAL for chemistry analysis and MYCIN for medical diagnosis. Expert systems are beneficial as they can store unlimited knowledge, work efficiently, and are unaffected by human limitations or emotions.
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.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
This document discusses handling uncertainty through probabilistic reasoning and machine learning techniques. It covers sources of uncertainty like incomplete data, probabilistic effects, and uncertain outputs from inference. Approaches covered include Bayesian networks, Bayes' theorem, conditional probability, joint probability distributions, and Dempster-Shafer theory. It provides examples of calculating conditional probabilities and using Bayes' theorem. Bayesian networks are defined as directed acyclic graphs representing probabilistic dependencies between variables, and examples show how to represent domains of uncertainty and perform probabilistic reasoning using a Bayesian network.
Fuzzy ARTMAP is a neural network architecture that uses fuzzy logic and adaptive resonance theory (ART) for supervised learning. It incorporates two fuzzy ART modules, ART-a and ART-b, linked together by an inter-ART module called the MAP field. This allows the network to form predictive associations between categories and track matches using a mechanism called match tracking. The match tracking recognizes category structures to avoid repeating predictive errors on subsequent inputs. Fuzzy ARTMAP is trained until it can correctly classify all training data by increasing the vigilance parameter of ART-a in response to predictive mismatches at ART-b.
This document provides an overview of database system concepts and architecture. It discusses data models, schemas, instances, and states. It also describes the three-schema architecture, data independence, DBMS languages and interfaces, database system utilities and tools, and centralized and client-server architectures. Key classification of DBMSs are also covered.
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.
This document provides an overview of Naive Bayes classification. It begins with background on classification methods, then covers Bayes' theorem and how it relates to Bayesian and maximum likelihood classification. The document introduces Naive Bayes classification, which makes a strong independence assumption to simplify probability calculations. It discusses algorithms for discrete and continuous features, and addresses common issues like dealing with zero probabilities. The document concludes by outlining some applications of Naive Bayes classification and its advantages of simplicity and effectiveness for many problems.
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 document discusses and provides examples of supervised and unsupervised learning. Supervised learning involves using labeled training data to learn relationships between inputs and outputs and make predictions. An example is using data on patients' attributes to predict the likelihood of a heart attack. Unsupervised learning involves discovering hidden patterns in unlabeled data by grouping or clustering items with similar attributes, like grouping fruits by color without labels. The goal of supervised learning is to build models that can make predictions when new examples are presented.
An overview of gradient descent optimization algorithms Hakky St
This document provides an overview of various gradient descent optimization algorithms that are commonly used for training deep learning models. It begins with an introduction to gradient descent and its variants, including batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent. It then discusses challenges with these algorithms, such as choosing the learning rate. The document proceeds to explain popular optimization algorithms used to address these challenges, including momentum, Nesterov accelerated gradient, Adagrad, Adadelta, RMSprop, and Adam. It provides visualizations and intuitive explanations of how these algorithms work. Finally, it discusses strategies for parallelizing and optimizing SGD and concludes with a comparison of optimization algorithms.
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.
-BayesianLearning in machine Learning 12Kumari Naveen
1. Bayesian learning methods are relevant to machine learning for two reasons: they provide practical classification algorithms like naive Bayes, and provide a useful perspective for understanding many learning algorithms.
2. Bayesian learning allows combining observed data with prior knowledge to determine the probability of hypotheses. It provides optimal decision making and can accommodate probabilistic predictions.
3. While Bayesian methods may require estimating probabilities and have high computational costs, they provide a standard for measuring other practical methods.
1. A perceptron is a basic artificial neural network that can learn linearly separable patterns. It takes weighted inputs, applies an activation function, and outputs a single binary value.
2. Multilayer perceptrons can learn non-linear patterns by using multiple layers of perceptrons with weighted connections between them. They were developed to overcome limitations of single-layer perceptrons.
3. Perceptrons are trained using an error-correction learning rule called the delta rule or the least mean squares algorithm. Weights are adjusted to minimize the error between the actual and target outputs.
The document discusses data preprocessing techniques. It explains that data preprocessing is important because real-world data is often noisy, incomplete, and inconsistent. The key techniques covered are data cleaning, integration, reduction, and transformation. Data cleaning handles missing values, noise, and outliers. Data integration merges data from multiple sources. Data reduction reduces data size through techniques like dimensionality reduction. Data transformation normalizes and aggregates data to make it suitable for mining.
The document discusses gradient descent methods for unconstrained convex optimization problems. It introduces gradient descent as an iterative method to find the minimum of a differentiable function by taking steps proportional to the negative gradient. It describes the basic gradient descent update rule and discusses convergence conditions such as Lipschitz continuity, strong convexity, and condition number. It also covers techniques like exact line search, backtracking line search, coordinate descent, and steepest descent methods.
This document discusses machine learning concepts like supervised and unsupervised learning. It explains that supervised learning uses known inputs and outputs to learn rules while unsupervised learning deals with unknown inputs and outputs. Classification and regression are described as types of supervised learning problems. Classification involves categorizing data into classes while regression predicts continuous, real-valued outputs. Examples of classification and regression problems are provided. Classification models like heuristic, separation, regression and probabilistic models are also mentioned. The document encourages learning more about classification algorithms in upcoming videos.
The document discusses machine learning algorithms for learning first-order logic rules from examples. It introduces the FOIL algorithm, which extends propositional rule learning algorithms like sequential covering to learn more general first-order Horn clauses. FOIL uses an iterative specialization approach, starting with a general rule and greedily adding literals to better fit examples while avoiding negative examples. The document also discusses how combining inductive learning with analytical domain knowledge can improve learning, such as in the KBANN approach where a neural network is initialized based on the domain theory before training on examples.
The word ‘stochastic‘ means a system or process linked with a random probability. Hence, in Stochastic Gradient Descent, a few samples are selected randomly instead of the whole data set for each iteration. In Gradient Descent, there is a term called “batch” which denotes the total number of samples from a dataset that is used for calculating the gradient for each iteration. In typical Gradient Descent optimization, like Batch Gradient Descent, the batch is taken to be the whole dataset. Although using the whole dataset is really useful for getting to the minima in a less noisy and less random manner, the problem arises when our dataset gets big.
Suppose, you have a million samples in your dataset, so if you use a typical Gradient Descent optimization technique, you will have to use all of the one million samples for completing one iteration while performing the Gradient Descent, and it has to be done for every iteration until the minima are reached. Hence, it becomes computationally very expensive to perform.
This problem is solved by Stochastic Gradient Descent. In SGD, it uses only a single sample, i.e., a batch size of one, to perform each iteration. The sample is randomly shuffled and selected for performing the iteration.
Expert System in Artificial Intelligences7118080008
An expert system is a computer program designed to solve complex problems like a human expert. It uses knowledge stored in its knowledge base and reasoning rules to determine solutions. The first expert system was developed in 1970. It consists of a user interface, inference engine, and knowledge base. The inference engine applies rules to the knowledge base to derive conclusions. Popular examples include DENDRAL for chemistry analysis and MYCIN for medical diagnosis. Expert systems are beneficial as they can store unlimited knowledge, work efficiently, and are unaffected by human limitations or emotions.
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.
Expert systems are computer programs that emulate human experts by using knowledge about a specific problem domain. An expert system consists of a knowledge base that contains rules and facts, and an inference engine that applies the rules to the known facts to deduce new facts. Expert systems can solve complex problems, provide explanations for their solutions, and serve as intelligent tutors. However, they are limited in their ability to generalize or reason about new situations not covered by their existing knowledge.
This proposal aims to develop an expert system to assist dermatologists in accurately diagnosing skin diseases. The system will acquire knowledge from experts and patients using techniques like interviews and surveys. It will represent this knowledge using decision trees and rules. A prototype will be tested on patient samples to evaluate its effectiveness.
MYCIN was one of the earliest and most influential expert systems developed in the 1970s. It helped physicians diagnose blood infections and recommend antibiotic treatments. The physician would enter patient data and MYCIN would analyze the information and provide diagnosis and treatment recommendations to assist the doctor. While very effective, MYCIN was not intended to replace physicians and still required final approval from medical experts.
Application of Expert Systems inSystem Analysis & Designfaiza nahin
The document discusses the application of expert systems in system analysis and design. It begins with an introduction to expert systems, including their history and structure. Expert systems use a knowledge base and inference engine to simulate human expert judgment. The document then discusses various applications of expert systems, including in production scheduling, medicine, banking/finance, manufacturing, and real-time process control. Specific examples provided include systems for cancer detection, loan approval, and aircraft diagnostics.
The document describes a decision support system designed to help physicians produce accurate prescriptions for patients with polyuria (excessive urination). The system uses an expert system shell called CLIPS to implement logical rules for polyuria treatment. The authors studied treatment rules, represented them in an antecedent/consequent model, and implemented the rules in CLIPS. They then evaluated the system using a double-blind technique and found it was able to produce prescriptions based on medical science while also allowing physician expertise and experience to be incorporated.
Artificial intelligence (AI) involves simulating human intelligence through computer programs. This document discusses various topics in AI including narrow, general, and strong AI; components of AI like data collection, reasoning, and learning; applications such as chatbots, expert systems, and machine learning. Expert systems use knowledge and inference rules to solve problems like a human expert would. They have a user interface, knowledge base, inference engine, and rules base. Machine learning uses algorithms that learn from examples to make predictions.
An expert system is software that uses a knowledge base of human expertise to solve problems or clarify uncertainties in areas where human experts would normally need to be consulted. It captures knowledge from subject matter experts and represents it in a structured way so it can provide automated guidance or recommendations. Expert systems are used in many fields like medicine, engineering, science, and business to emulate the problem-solving abilities of human experts.
The document discusses expert systems, which use artificial intelligence to simulate human judgment. An expert system consists of a knowledge base containing accumulated experience and an inference engine with rules for applying the knowledge. Expert systems are needed due to limitations of human decision-making like scarce expertise and inconsistencies. They have benefits like increasing the probability of good decisions, distributing expertise, and enabling objective decisions without human bias.
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 document discusses expert systems, including definitions, types of knowledge, characteristics, participants in development, structure, and applications. It defines expert systems as attempting to imitate an expert's knowledge and reasoning to solve specific problems. The key components are a knowledge base, inference engine, user interface, and explanation facility. Applications include medical diagnosis, mineral exploration, and natural language interfaces.
This document discusses an assignment submitted on expert system design. It begins with defining an expert system as a computer application that performs tasks done by human experts, such as medical diagnosis. It then outlines the advantages and disadvantages of using expert systems. Key advantages include providing consistent solutions, reasonable explanations, and overcoming human limitations. Disadvantages include lacking common sense, high costs, and difficulties creating inference rules. The document also differentiates expert systems from conventional computer systems and describes knowledge acquisition in expert systems as the process of extracting and organizing knowledge from human experts. It discusses forward and backward chaining and declarative versus procedural knowledge. Finally, it presents problems on analyzing a circuit output and applying laws of equivalence to a logical expression, and defining a
Artificial intelligence began in the 1960s with early attempts at game playing, theorem proving, and problem solving. An expert system is a type of AI that attempts to provide answers to problems where human experts would normally be consulted. Expert systems use knowledge bases, inference engines, and other components to mimic human expertise in a specific domain. Virtual reality allows users to interact with simulated environments through technologies like head-mounted displays, CAVEs, and haptic interfaces.
An expert system is an intelligent computer program that uses knowledge and inference procedures to solve problems that typically require significant human expertise. It consists of a knowledge base containing expertise and an inference engine that draws conclusions. Expert systems can assist humans when experts are unavailable, replace human decision making, and are often used in applications like accounting, geology, and medical diagnosis. However, expert systems also have limitations like being limited to narrow problems and not learning on their own.
The document discusses expert systems, which are intelligent computer programs that use knowledge and inference procedures to solve problems that require significant human expertise. It describes how expert systems consist of a knowledge base containing knowledge and an inference engine that draws conclusions. It provides examples of applications like medical diagnosis and mining site selection. It outlines the key components of an expert system and discusses their benefits in replacing human experts as well as limitations like limited domains and high development costs.
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.
It gives an individual a general Idea about Expert System and the wide variety of it's Applications.We discuss the scope of Expert System in upcoming Future in various Domains and various Challenges.Some examples are also given of a few Expert Systems.
Expert systems are AI programs that use knowledge bases to achieve expert-level competence in solving problems in a particular task domain. They consist of two main components: a knowledge base containing factual and heuristic knowledge about the domain, and an inference engine that uses reasoning methods like backward and forward chaining to derive answers. Expert systems have applications in fields like medicine, agriculture, and education, where they can provide consistent advice and help clarify decision making. However, they lack common sense and cannot respond creatively to unusual situations.
BI UNIT V CHAPTER 12 Artificial Intelligence and Expert System.pptxTGCbsahil
The document discusses concepts and components of artificial intelligence (AI) and expert systems. It defines AI as concerned with studying human thought processes and duplicating them in machines. Expert systems are computer-based systems that use expert knowledge to make decisions. The key components of expert systems are the knowledge base containing rules and heuristics, the inference engine that interprets rules to solve problems, and the user interface. Common applications of expert systems include medical diagnosis, credit analysis, and market surveillance.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Module -3 expert system.pptx
1. Module -3
Expert Systems in AI
Course teacher:
Dr. S. Syed Rafiammal
Assistant Professor, ECE department
BS Abdur Rahman Crescent Institute of Science and Technology
2. What is an Expert System?
• An expert system is a computer program that
is designed to solve complex problems and to
provide decision-making ability like a human
expert.
• It performs this by extracting knowledge from
its knowledge base using the reasoning and
inference rules according to the user queries.
4. Motive
• The system helps in decision making for
complex problems using both facts and
heuristics like a human expert.
• It is called so because it contains the expert
knowledge of a specific domain and can solve
any complex problem of that particular
domain.
• These systems are designed for a specific
domain, such as medicine, science, etc.
5. Components of Expert System
https://www.javatpoint.com/expert-
systems-in-artificial-intelligence
6. User Interface
• It is an interface that helps a non-expert user
to communicate with the expert system to find a
solution
• With the help of a user interface, the expert
system interacts with the user, takes queries as
an input in a readable format, and passes it to the
inference engine.
• After getting the response from the inference
engine, it displays the output to the user.
7. Inference Engine(Rules of Engine)
• The inference engine is known as the brain of the expert system as
it is the main processing unit of the system.
• It applies inference rules to the knowledge base to derive a
conclusion or deduce new information.
• It helps in deriving an error-free solution of queries asked by the
user.
• With the help of an inference engine, the system extracts the
knowledge from the knowledge base.
There are two types of inference engine:
• Deterministic Inference engine: The conclusions drawn from this
type of inference engine are assumed to be true. It is based
on facts and rules.
• Probabilistic Inference engine: This type of inference engine
contains uncertainty in conclusions, and based on the probability.
8. Knowledge Base
• The knowledgebase is a type of storage that stores
knowledge acquired from the different experts of the
particular domain. It is considered as big storage of
knowledge. The more the knowledge base, the more
precise will be the Expert System.
• It is similar to a database that contains information and
rules of a particular domain or subject.
• One can also view the knowledge base as collections of
objects and their attributes. Such as a Lion is an object
and its attributes are it is a mammal, it is not a
domestic animal, etc.
9. Components of Knowledge Base
• Factual Knowledge: The knowledge which is
based on facts and accepted by knowledge
engineers comes under factual knowledge.
• Heuristic Knowledge: This knowledge is based
on practice, the ability to guess, evaluation,
and experiences.
10. Knowledge Representation &
Acquisitions
• Knowledge Representation: It is used to
formalize the knowledge stored in the
knowledge base using the If-else rules.
• Knowledge Acquisitions: It is the process of
extracting, organizing, and structuring the
domain knowledge, specifying the rules to
acquire the knowledge from various experts,
and store that knowledge into the knowledge
base.
11. Example Scenario
• Suppose a patient comes to a medical clinic
with symptoms like fever, sore throat, and
swollen lymph nodes. The doctor, who is using
an expert system for assistance, inputs these
symptoms into the system. Here's how the
expert system might work:
12. Example Scenario- Solution
• Data Input Inference Engine Knowledge Base
Diagnosis Rule-Based Reasoning
Recommendations Explanations
Procedure:
1. The doctor enters the patient's symptoms into the expert
system through the user interface.
2. The inference engine starts to analyze the input
symptoms. It begins by asking questions or making
hypotheses. For instance, it might ask, "Is the patient
experiencing difficulty swallowing?" Based on the
responses, it narrows down the possibilities.
13. Procedure
• 3. The expert system consults its knowledge base, which
contains information about various diseases and their
symptoms. It knows that fever, sore throat, and swollen
lymph nodes can be indicative of several diseases, including
strep throat, mononucleosis, and tonsillitis.
• 4. The system applies rules and logical reasoning to the
information in the knowledge base. It might have rules like
"If the patient has a fever and swollen lymph nodes but no
difficulty swallowing, it could be mononucleosis.“
• 5. Based on the input symptoms, responses to questions,
and the application of rules, the expert system generates a
preliminary diagnosis. In this case, it suggests that the
patient might have mononucleosis.
14. Procedure
• 6. Recommendations: The expert system
provides treatment recommendations, which
may include rest, fluids, and antiviral
medications. It may also advise the doctor to run
further tests for confirmation.
• 7. Explanations: Importantly, expert systems can
provide explanations for their conclusions. It can
explain to the doctor why it arrived at the
diagnosis of mononucleosis, citing the specific
symptoms and rules it applied.
15. Examples
• DENDRAL: It was an artificial intelligence project that was made as
a chemical analysis expert system. It was used in organic chemistry
to detect unknown organic molecules with the help of their mass
spectra and knowledge base of chemistry.
• MYCIN: It was one of the earliest backward chaining expert systems
that was designed to find the bacteria causing infections like
bacteria and meningitis. It was also used for the recommendation
of antibiotics and the diagnosis of blood clotting diseases.
• PXDES: It is an expert system that is used to determine the type and
level of lung cancer. To determine the disease, it takes a picture
from the upper body, which looks like the shadow. This shadow
identifies the type and degree of harm.
• CaDeT: The CaDet expert system is a diagnostic support system that
can detect cancer at early stages.
16. Examples
• CLIPS: The C Language Integrated Production System (CLIPS) is a widely used open-source
expert system development tool. It provides a framework for building rule-based expert
systems and has applications in various fields, including aerospace and healthcare.
• XCON: XCON is a well-known expert system developed by Digital Equipment Corporation
(DEC) in the 1980s. It was used for configuring computer systems based on customer
requirements.
• PROSPECTOR: PROSPECTOR is an expert system used in the field of mineral exploration. It
helps geologists identify potential mining sites based on geological data and expert
knowledge.
• CADUCEUS: CADUCEUS is an expert system developed for diagnosing certain medical
conditions, specifically blood disorders. It combines rule-based reasoning with probabilistic
methods to make diagnoses.
• Cyc: Cyc is a long-term project aimed at creating a comprehensive, common-sense
knowledge base that can be used to power various expert systems. It's been applied in areas
like natural language processing and automated reasoning.
• Watson: Developed by IBM, Watson is a more recent and well-known example of an expert
system. It gained fame for winning the quiz show Jeopardy! in 2011. Watson is used in
various applications, including healthcare (IBM Watson Health) and business analytics.
• DeepMind's AlphaFold: While not a traditional expert system, AlphaFold is an AI system that
excels in predicting the 3D structures of proteins, a problem that has stumped scientists for
decades. It demonstrates the power of AI and expert knowledge in a critical area of biology.
17. Examples
• Cyc: Cyc is a long-term project aimed at creating a comprehensive,
common-sense knowledge base that can be used to power various
expert systems. It's been applied in areas like natural language
processing and automated reasoning.
• Watson: Developed by IBM, Watson is a more recent and well-
known example of an expert system. It gained fame for winning the
quiz show Jeopardy! in 2011. Watson is used in various applications,
including healthcare (IBM Watson Health) and business analytics.
• DeepMind's AlphaFold: While not a traditional expert system,
AlphaFold is an AI system that excels in predicting the 3D structures
of proteins, a problem that has stumped scientists for decades. It
demonstrates the power of AI and expert knowledge in a critical
area of biology.
18. Probability-based expert system
• A probability-based expert system is a type of artificial
intelligence system that uses probability theory and
statistical methods to make decisions or provide
recommendations.
• It combines expert knowledge with probabilistic
reasoning to handle uncertainty and make informed
choices.
• These systems are commonly used in various fields,
including medicine, finance, and engineering, where
decisions must be made in situations with incomplete
or uncertain information.
20. Components of a Probability-Based
Expert System
• Knowledge Base: This contains the domain-specific
information and rules provided by experts. It includes
facts, relationships, and conditional probabilities.
• Inference Engine: The inference engine is responsible
for reasoning and making decisions based on the
information in the knowledge base. It uses probabilistic
reasoning techniques to calculate the likelihood of
different outcomes.
• User Interface: This component allows users to interact
with the expert system, input data, and receive
recommendations or decisions.
21. Example 1: Medical Diagnosis Expert
System
Let's consider an example of a medical diagnosis expert
system that uses probabilities to help diagnose a patient's
condition.
• Knowledge Base: The knowledge base contains
information about symptoms, diseases, and the
likelihood of a patient having a particular disease given
their symptoms. For instance, it might include the
following rules:
• If the patient has a high fever AND a persistent cough,
THEN the probability of having the flu is 70%.
• If the patient has a rash AND joint pain, THEN the
probability of having a viral infection is 50%.
22. Inference Engine
• The inference engine takes patient input (symptoms) and
calculates the probabilities of various diseases.
• It combines these probabilities to determine the most
likely diagnosis. It may use techniques like Bayesian
networks or Markov models to perform these calculations.
• For example,
if a patient reports having a high fever and a
persistent cough, the inference engine might calculate the
probability of having the flu as 70%. If the patient also reports
a rash and joint pain, it would consider the probabilities of all
relevant diseases and assign the highest probability to the
most likely diagnosis.
23. User Interface
• The user interface allows the patient or healthcare
provider to input the patient's symptoms. After
processing the input, the expert system would provide
a ranked list of possible diagnoses along with their
associated probabilities. It might say something like:
• Diagnosis 1: Influenza (70% probability)
• Diagnosis 2: Viral infection (50% probability)
• Diagnosis 3: Common cold (30% probability)
• The healthcare provider can then use this information
to make an informed decision about further testing or
treatment.
24. Conclusion
• In this example, the probability-based expert
system uses probabilistic reasoning to handle
the uncertainty associated with medical
diagnoses.
• It provides a quantitative assessment of the
likelihood of different outcomes, allowing for
more informed decision-making in complex
and uncertain situations.
25. Example 2: Autonomous Vehicle
Decision-Making Expert System
Knowledge Base:
• The knowledge base in an autonomous vehicle decision-making
expert system contains a vast amount of data related to road
conditions, traffic rules, vehicle performance, and potential hazards.
• It includes rules and probabilities associated with various driving
scenarios.
• For instance:
• Probability of pedestrians crossing the road at a crosswalk during
daylight hours.
• Rules for maintaining safe following distances behind other vehicles
under different weather conditions.
• Probabilities of encountering road construction or accidents on a
particular route.
26. Inference Engine
• The inference engine continuously processes sensor data
from the vehicle, including information from cameras,
LiDAR, radar, and GPS.
• It combines this real-time data with the knowledge base to
make decisions about the vehicle's speed, lane changes,
braking, and other driving actions.
• For example,
if the LiDAR sensor detects a pedestrian at a crosswalk, the
inference engine calculates the probability of a collision and
determines if the vehicle should slow down or stop to avoid
the pedestrian. It takes into account factors such as the
vehicle's speed, the pedestrian's location, and the current
weather and road conditions.
27. Example 3: Investment Portfolio
Management Expert System
• Knowledge Base: The knowledge base in this expert
system contains financial data, historical market trends,
and rules for portfolio management.
For instance:
• Historical data on the performance of various asset
classes like stocks, bonds, and real estate.
• Rules for asset allocation, such as "If the investor has a
high risk tolerance, allocate 70% to stocks and 30% to
bonds."
• Probabilities associated with market conditions, such
as "There is a 30% probability of a stock market
downturn in the next year."
28. Inference Engine:
• The inference engine takes input from the investor, including their
risk tolerance, investment goals, and current financial situation. It
then uses this information to make investment recommendations
based on probabilistic reasoning.
• For example, if an investor with a moderate risk tolerance and a
long-term investment horizon seeks advice, the inference engine
might recommend a portfolio allocation like this:
• Stocks: 60%
• Bonds: 30%
• Real Estate: 10%
• The engine calculates these recommendations by considering the
investor's risk tolerance and the historical performance data and
probabilistic market conditions.
29. Expert system Tools
• Expert system tools are software or platforms
designed to facilitate the development,
deployment, and management of expert systems.
• These tools provide a range of functionalities,
including knowledge representation, inference
engines, user interfaces, and sometimes machine
learning capabilities.
• They simplify the process of building and
maintaining expert systems, making it easier for
domain experts to codify their knowledge and for
organizations to leverage that expertise.
30. List of some notable expert system
tools:
• CLIPS: CLIPS (C Language Integrated Production System) is a widely
used open-source expert system development tool that provides a
rule-based inference engine and knowledge representation.
• Drools: Drools is a popular open-source rule engine written in Java.
It offers rule-based programming capabilities and is used for
building decision-management systems and expert systems.
• Jess: Jess (Java Expert System Shell) is another rule engine and
scripting environment for the Java platform. It is similar in
functionality to CLIPS and is used for rule-based programming.
• Prolog: Prolog is a logic programming language commonly used for
building expert systems based on rule-based reasoning and
knowledge representation.
31. Expert system tools
• Pyke: Pyke is a knowledge-based inference engine that
integrates with Python. It allows you to build rule-based
expert systems using Python's scripting capabilities.
• IBM Watson Knowledge Studio: IBM's Watson Knowledge
Studio provides tools for building machine learning-based
expert systems by annotating and training data. It's
especially useful for natural language understanding
applications.
• Exsys Corvid: Exsys Corvid is a commercial expert system
development tool that offers a range of features for
building decision support systems and expert systems.
32. Expert system tools
• NeuroRule: NeuroRule is a tool that combines neural
networks with expert systems. It's designed for
applications where both symbolic reasoning and neural
network-based learning are required.
• XCON: XCON (Expert Configurer) is a historic example
of an expert system that was developed by Digital
Equipment Corporation (DEC) for configuring VAX
computer systems. While not a contemporary tool, it's
noteworthy in the history of expert systems.
• OpenRules: OpenRules is an open-source business
rules management system that provides a rule engine
for building decision logic and expert systems.
33. Expert system tools
• Fuzzy Logic Tools: Various tools and libraries,
such as MATLAB's Fuzzy Logic Toolbox and scikit-
fuzzy for Python, offer support for building expert
systems that utilize fuzzy logic for handling
uncertainty.
• Inference Engines in AI Platforms: Many AI
platforms and frameworks, such as TensorFlow,
PyTorch, and Kie, include components for
building rule-based expert systems or integrating
rule-based reasoning into machine learning
applications.
34. Example
• An example of an expert system using an
inference engine in an AI platform can involve
utilizing rule-based reasoning within a
machine learning framework.
• In this example, we'll use Python and
TensorFlow/ Keras, which are popular AI
platforms, to build an expert system for
diagnosing plant diseases based on leaf
images.
35. Problem Statement: Develop an expert system
to diagnose plant diseases using leaf images.
• Steps to Create the Expert System:
• Data Collection: Collect a dataset of leaf images, where each image
is labeled with the type of disease or is healthy.
• Rule-Based Component: Define a set of rules that consider visual
symptoms of diseases. For instance:
if yellow_spots and curling_leaves:
diagnosis = "Yellow Leaf Curl Virus"
elif brown_spots and wilted_leaves:
diagnosis = "Fungal Infection"
else:
diagnosis = "Healthy"
• These rules are based on visual symptoms observed in the leaf
images.
36. • Machine Learning Component: Use a neural network (in
this case, a convolutional neural network or CNN) to learn
from the images. Train the model on the labeled dataset to
classify leaf images into healthy or disease categories.
• Inference Engine: Implement an inference engine that
combines the output from the rule-based component and
the machine learning component. It can be as simple as
considering the rule-based diagnosis unless the confidence
of the neural network prediction is high.
if confidence >= 0.8:
diagnosis = neural_network_prediction
37. Example Python program for Rule based expert system
with machine learning
# Rule-Based Component
def rule_based_diagnosis(leaf_image):
# Implement rules to diagnose based on visual symptoms
# Example: Check for yellow spots, curling leaves, brown
spots, wilted leaves, etc.
if yellow_spots and curling_leaves:
return "Yellow Leaf Curl Virus"
elif brown_spots and wilted_leaves:
return "Fungal Infection"
else:
return "Healthy"
38. # Machine Learning Component (Neural Network)
def neural_network_diagnosis(leaf_image):
# Use a pre-trained CNN model or train one on the dataset
# Return the predicted class (disease or healthy)
# Inference Engine
def expert_system_diagnosis(leaf_image):
rule_based = rule_based_diagnosis(leaf_image)
neural_network, confidence = neural_network_diagnosis(leaf_image)
if confidence >= 0.8:
return neural_network
else:
return rule_based
39. # Load an image and perform diagnosis
leaf_image = load_image("sample_leaf.jpg")
diagnosis=expert_system_diagnosis(leaf_image)
print("Diagnosis:", diagnosis)
40. Conclusion
• In this example, we've combined a rule-based
approach (based on visual symptoms) and a
neural network-based approach (using
TensorFlow/Keras) within an inference engine.
• The expert system can provide a diagnosis
based on both rules and machine learning
predictions, with a confidence threshold to
decide which diagnosis to trust.