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6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
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Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Expert Systems in AI

Ayushi Trivedi
Ayushi Trivedi Last Updated : 12 Aug, 2024
7 min read

Introduction

Just consider the fact that you can always consult with an advisor and get advanced level and quality solutions to tackling issues that range from diseases to financial planning to engineering. This is the role of experts in systems in artificial intelligence. Knowledge-based systems simulate the capabilities of a human expert, providing the user with recommendation and information that are inferred from voluminous information. In this article, our goal is to provide the reader with a comprehensive understanding of what expert systems are, how they operate, and how they are implemented across various fields. By the end, you will be aware of what expert systems are and the major advantages and disadvantages of their functioning in AI.

Expert systems in AI

Overview

  • Understand what expert systems are and their role in AI.
  • Learn the components that make up expert systems.
  • Discover how expert systems operate.
  • Explore various applications of expert systems in different fields.
  • Recognize the advantages and limitations of using expert systems.
  • Gain insights into the future of expert systems in AI.

Table of contents

What Are Expert Systems?

Expert systems are a branch of AI designed to emulate the decision-making capabilities of human experts. These systems use a knowledge base of human expertise and an inference engine to solve specific problems or provide advice. They are typically used in fields where human expertise is limited or expensive, providing valuable assistance in complex decision-making processes.

Expert system

Why Expert Systems?

Expert systems offer several advantages that make them highly valuable in various domains:

  • No Emotions: Unlike human experts, expert systems operate purely on logic and data without emotional biases, ensuring impartiality in decision-making.
  • High Efficiency: They can process vast amounts of data quickly and accurately, delivering solutions faster than human experts.
  • Expertise in a Domain: Expert systems are designed with in-depth knowledge in specific fields, providing expert-level advice and solutions.
  • No Memory Limitation: They can store and recall extensive amounts of information without the limitations of human memory.
  • High Security: You can design expert systems with advanced security measures to protect sensitive information and ensure data integrity.
expert systems

Components of Expert Systems

Let us look into the components of expert systems.

Knowledge Base

Knowledge base is an essential part of the expert system containing all the information and rules, facts, and relation required for the solution of problems in a specific domain. It consists of two main parts: book knowledge and street knowledge. The content is the codified knowledge that is fixed and consists of the facts about the domain and the heuristic knowledge that represents the working rules and operational procedures used by the professionals to solve problems. For instance, in development of an expert system in medical diagnosis, the knowledge base would entail medical conditions, symptoms, diagnostics and treatment.

Inference Engine

The inference engine is the working engine of an expert system. This engine also has knowledge and fact databases to process. It carries out an inference process on the knowledge base using logical rules to arrive at new facts and conclusions. It operates using two primary methods: There are two types of message passing these are the forward chaining and backward chaining. Hypothetically, forward chaining works from the data that is available and, following the rules, derives other data until it arrives at a conclusion.

In backward chaining, one begins with possible conclusions that can be drawn for a particular problem and looks to determine whether any of the given information support such conclusions. A more concrete example in a financial advisory expert system, the inference engine may use a forward chaining technique where it begins with a client’s information on her financial status, applying rules to come to the best solution for investing.

User Interface

The user interface is the medium through which users interact with the expert system. It allows users to input data, query the system, and receive the system’s advice or solutions. A good user interface is crucial for making the expert system accessible and user-friendly, ensuring that users can efficiently input their queries and understand the system’s outputs. For instance, a medical expert system’s user interface might include forms for entering patient symptoms and history, as well as dashboards displaying diagnostic results and treatment recommendations.

Explanation Facility

The explanation facility is a component that helps users understand how the expert system arrived at a particular conclusion or recommendation. It provides a step-by-step explanation of the reasoning process, making the system’s decision-making transparent and building user trust. In a legal advisory expert system, the explanation facility might outline the rules and precedents applied to reach a legal decision or recommendation.

Knowledge Acquisition Module

The knowledge acquisition module is responsible for updating the knowledge base with new information and expertise. It allows the system to evolve and stay current by incorporating new data, rules, and heuristics. This can be done manually by domain experts or through automated learning techniques. In an agricultural expert system, the knowledge acquisition module might integrate new research findings on crop diseases and pest control methods.

How Expert Systems Operate

Expert systems follow a structured process to emulate human decision-making:

  • Input: Users input data or questions through the user interface.
  • Knowledge Application: The inference engine processes this data using the rules and facts stored in the knowledge base.
  • Reasoning: The system applies logical reasoning to draw conclusions or provide recommendations.
  • Output: The system presents the solution or advice to the user, often with an explanation of how it arrived at the conclusion.

Applications of Expert Systems

Various industries use expert systems to process vast amounts of information and provide expert-level advice.

  • Medical Diagnosis: Others are those that aid in diagnosis of diseases and prescription of appropriate treatment from information that you feed into the system such as MYCIN.
  • Financial Services: Expert systems help in the decision-making process in investments, evaluation of risks, and identification of fraud.
  • Engineering: Aids in development of methods, identification of problems and improvement on the existing procedures.
  • Customer Support: The ideal form of such a service is to deliver expert-level automated response to customers’ questions and requests.
  • Agriculture: Help the farmers with techniques regarding their crops, pest, and soils among others.

Future of Expert Systems

Looking to the future of the development of the expert systems in the given branch of artificial intelligence, one can identify several essential trends which can promote the enhancement of the field. Advancements in technology and research will improve expert systems by incorporating refined features and tackling complex problems in different fields.

Integration with Machine Learning and Big Data

One of the most defining developments in expert systems will be their integration with machine learning and big data. Expert systems can be improved by adding new machine learning algorithms. These algorithms can process large amounts of data to discover valuable techniques. This integration will allow expert systems to learn from new data and update themselves autonomously. It will increase their efficiency in delivering accurate results. For example, in the medical field, integrating expert systems with machine learning can help classify patient data from electronic health records. It can also offer more suitable treatment regimens and identify potential epidemics.

Natural Language Processing

Forthcoming developments in NLP will improve expert systems’ user interfaces. This will create expert systems with clean interfaces. Integration of natural language will make it easy for non-expert users. They will be able to type queries and understand results easily. This will be especially effective in customer relations. Expert systems will solve intricate questions and explain answers in simple terms.

Internet of Things

In this way, IoT devices will flood expert systems with actual data from multiple sources. Expert systems through this real-time data will be in a better position to make correct decisions as well as timely advices. For instance, in smart agriculture, ES operationalize IoT sensors to acquire information concerning the status of the soil, atmospheric condition and health of crops, and present solutions to the farmers.

Enhanced Explainability and Trust

In future, with the increasing complexity of the expert systems, loglication and explainability criteria will become even more important. Users will want to know how these systems make decisions because fields such as health, finance, and law require clear and justifiable conclusions. Future expert systems will also have better methods of explaining their decisions to the user by giving detailed decision-making procedures. This will help to establish confidence which will make the users depend on these systems in their decision making.

Domain-Specific Expert Systems

The future will see more specialized expert systems tailored to specific business fields. These systems will include domain-specific knowledge for decision-making. They will handle tasks that generalized systems cannot. For example, in cybersecurity, expert systems will analyze network traffic, identify threats, and suggest actions.

Autonomous Expert Systems

Expert systems in the future shall be capable of making decisions on their own without any controlled by man. These systems will prove especially important in such applications that depend on quick decisions, like autonomic transport, industry, and crisis management. Real-time data, sophisticated computations, and sound Decision Analytical Architectures will be relevant in the functioning of these expert systems to the extent that their operation would be safe.

Ethical and Regulatory Considerations

Even now, as expert systems unite various domains, ethical and regulatory factors are crucial. Ensuring these systems are ethical at conception and use is vital. They must respect user confidentiality, justice, and transparency. Governance structures must evolve. General regulations are no longer sufficient. We need clear guidelines to use expert systems safely and responsibly.

Advantages of Expert Systems

  • Consistency: They provide consistent answers, reducing human error.
  • Availability: Accessible 24/7, providing continuous support.
  • Efficiency: Quickly process vast amounts of data to offer timely solutions.
  • Cost-Effective: Reduce the need for human experts, lowering operational costs.

Limitations of Expert Systems

  • Lack of Common Sense: They cannot understand context beyond their programmed knowledge.
  • Maintenance: Require regular updates to the knowledge base.
  • Limited Creativity: Cannot generate innovative solutions beyond their programmed rules.
  • Dependency on Quality of Data: The accuracy of their advice depends on the quality of the data in the knowledge base.

Conclusion

Expert systems can be seen as a major step forward in the AI area and apply expert solutions to most fields. Thus, the rationale is based on the analogy of human decision-making, providing decision-making consistency, speed, and cost-saving. However, they also include shortcoming like they do not incorporate commonsense, for the AI to work properly, it has to undergo an update now and again. It can be therefore suggested that, in time, expertise systems will become more advanced. And moreover it will widespread in the several a variety of fields to help with decision makings.

Frequently Asked Questions

Q1. What is an expert system?

A. An expert system is an AI program that mimics the decision-making abilities of a human expert in a specific field.

Q2. How do expert systems work?

A. They use a knowledge base of facts and rules, processed by an inference engine, to provide solutions or advice.

Q3. Where are expert systems used?

A. They are used in various fields, including medicine, finance, engineering, customer support, and agriculture.

Q4. What are the advantages of expert systems?

A. They offer consistency, efficiency, 24/7 availability, and cost savings.

Q5. What are the limitations of expert systems?

A. They lack common sense, require regular maintenance, have limited creativity, and depend on the quality of their knowledge base.

Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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