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所在平台: Udemy |
课程主页: https://www.udemy.com/course/adversarial-search-in-intelligent-knowledge-representation/
课程评论：没有评论
《智能知识表示中的对抗搜索》课程总结 本课程旨在为学生提供人工智能（AI）概念的坚实基础，特别是知识表示及其与机器学习（ML）的结合，以实现制造业和自动化系统中的智能决策。 课程首先介绍**知识表示**，重点关注世界信息如何被结构化以及为智能代理所利用。**命题逻辑**和**一阶逻辑（FOL）**等主题为学习者提供了正式的推理和推断工具。学生还将学习**基于知识的代理**，它们利用存储的知识在动态环境中感知、推理和行动。此外，课程深入探讨**对抗搜索**，将**Minimax算法**和**评估函数**等技术应用于竞争性、多代理场景，如游戏或战略决策系统。最后，学生和研究人员将分析实时应用以及实施稳健且可扩展知识表示系统的实际挑战。 第二模块将重点转移到**智能加工中的机器学习应用**，从**智能机器学习**的概述开始，介绍加工系统如何从传统自动化演变为由AI驱动的制造。课程涵盖使用**线性回归**进行预测建模，并强调**特征选择**和**数据预处理**，这是构建有效ML模型至关重要的步骤。学生和研究人员还将接触**支持向量机（SVM）**，用于加工应用中的分类和回归任务。本单元还探讨了ML在加工中的实际应用，如刀具磨损预测、质量控制和工艺优化。课程最后通过广泛使用的ML库**Scikit-learn**和**TensorFlow**进行实践操作，并结合源自真实加工数据集的**案例研究**，使学生能够理解智能系统如何在工业环境中部署。
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.
