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.
Who this course is for
Beginner python developer interested in machine Learning and Data science