Adversarial Search In Intelligent Knowledge Representation

Strategic Decision-Making with Logical Reasoning

What you'll learn

Understand foundational concepts of knowledge representation

Analyze and implement adversarial search techniques

Capable of making rational decisions based on logical rules and dynamic knowledge bases

Apply intelligent reasoning techniques to real-world problems

Requirements

No Programming needed

Description

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.

Overview

Section 1: Knowledge Representation and Adversarial Search

Lecture 1 Introduction to Knowledge Representation

Lecture 2 Knowledge-Based Agents

Lecture 3 Propositional Logic

Lecture 4 First-Order Logic (FOL)

Lecture 5 Adversarial Search - Minimax Algorithm

Lecture 6 Adversarial Search - Evaluation functions

Lecture 7 Real-Time Applications and Challenges of Knowledge Representation

Section 2: Intelligent Machining Using Machine Learning

Lecture 8 Introduction to Intelligent Machine Learning

Lecture 9 Types of Intelligent Machine Learning and Evolution of Intelligent Machining

Lecture 10 Machine Learning for Machining - Use of Linear Regression for predictive model

Lecture 11 Support Vector Machines (SVM)

Lecture 12 Applications in Intelligent Machining

Lecture 13 Case studies from real-world machining datasets

Lecture 14 Challenges of Intelligent Machining

Beginner python developer interested in machine Learning and Data science