
Knowledge Representation enables AI systems to store, organize, and reason with structured real-world information.
Knowledge-Based Agents use stored knowledge and reasoning to make intelligent decisions in dynamic environments.
Propositional Logic represents facts using logical statements and connectives to enable reasoning and decision-making.
First-Order Logic extends propositional logic by using variables, predicates, and quantifiers for detailed knowledge representation.
Minimax Algorithm evaluates game states in adversarial search to choose optimal moves for intelligent agents.
Evaluation functions estimate the value of game positions to guide decisions in adversarial search algorithms.
Real-time applications use knowledge representation for decision-making, facing challenges in scalability, consistency, and uncertainty.
Intelligent Machine Learning enables systems to learn from data, adapt, and make autonomous decisions efficiently.
Intelligent machining evolved from manual methods to adaptive, data-driven systems using AI and machine learning.
Linear Regression in machining predicts outcomes like tool wear or surface finish from input process variables.
Support Vector Machines classify data by finding the optimal boundary separating different classes in feature space.
Intelligent machining applications include tool wear prediction, process optimization, fault detection, and quality control automation.
Real-world machining case studies demonstrate how machine learning improves accuracy, efficiency, and decision-making in manufacturing
Machine learning faces challenges like data quality, model overfitting, interpretability, scalability, and computational resource limitations.
This course is designed to provide students with a strong foundation in Artificial Intelligence (AI) concepts, particularly in Knowledge Representation and its integration with Machine Learning (ML) for intelligent decision-making in manufacturing and automation systems. It introduces key AI principles beginning with Knowledge Representation, focusing on how information about the world can be structured and utilized by intelligent agents. Topics such as Propositional Logic and First-Order Logic (FOL) equip learners with formal tools for reasoning and inference. Students also explore Knowledge-Based Agents, which use stored knowledge to perceive, reason, and act in dynamic environments. It further delves into Adversarial Search, where techniques like the Minimax Algorithm and evaluation functions are applied to competitive, multi-agent scenarios such as games or strategic decision-making systems.Finally Students and Researchers analyze real-time applications and the practical challenges of implementing robust and scalable knowledge representation systems.The second module shifts focus to the application of Machine Learning in Intelligent Machining, starting with an overview of Intelligent Machine Learning and the evolution of machining systems from traditional automation to AI-powered manufacturing. It covers the use of Linear Regression for predictive modeling and emphasizes feature selection and data preprocessing, which are critical steps in building effective ML models. Students and Researchers are introduced to Support Vector Machines (SVMs) for classification and regression tasks in machining applications. The unit also explores practical applications of ML in machining, such as tool wear prediction, quality control, and process optimization. The course concludes with hands-on exposure to widely-used ML libraries like Scikit-learn and TensorFlow, along with case studies derived from real-world machining datasets, allowing students to understand how intelligent systems are deployed in industrial environments.