Planning with Artificial Intelligence

Name: Planning with Artificial Intelligence
Rating: 3.6 (14 reviews)

A Practical Approach to AI-Based Planning Methods

Created byDrUsha G

Last updated 6/2025

English

English [Auto],

What you'll learn

Understand and apply machine learning techniques such as Linear Regression and Support Vector Machines (SVM) to solve real-world classification and regression p
Explain the architecture and functionality of Expert Systems, and analyze their role in knowledge-based decision-making processes.
Analyze and implement problem-solving strategies in AI, including Means-End Analysis and Goal Stack Planning, for automated reasoning and goal achievement.
Demonstrate problem representation and planning using classical AI methods, with specific emphasis on structured problems like the Block World Problem.
Evaluate different AI planning techniques and apply them in deterministic and goal-driven environments for intelligent agent design.
Integrate concepts from machine learning and AI planning to build hybrid intelligent systems capable of learning from data and planning actions to achieve goals

Course content

1 section • 8 lectures • 1h 29m total length

Planning in Artificial Intelligence14:54
Explore planning in artificial intelligence, including classical and non-classical planning, with domains, initial states, and goals guiding agents from perception to action; compare planning and problem solving.
Block World Problem11:57
Learn how to solve the block world problem with Strips planning, including preconditions, effects, and robot hand actions like unstack, pick up, put down, and stack.
Goal Stack Planning for AI7:30
Learn goal stack planning for block world problems, starting from the goal state and using predicates and subgoal stacks with operations like pickup, put down, and unstack.
Mean End Analysis6:16
Explore means-end analysis, a hybrid forward-backward search strategy in AI that solves problems by solving small subproblems and selecting operators to minimize differences from initial to goal state.
Expert System8:46
Explore expert systems that emulate human decision making with a knowledge base, inference engine, and user interface, using forward and backward chaining and rules.
Machine Learning12:40
Discover how machine learning uses historical data and features to train models, with supervised, unsupervised, semi-supervised, and ensemble methods, plus real-world applications and challenges.
Linear Regression18:26
Explore linear regression, a supervised learning technique that maps input variables to continuous outputs via a best-fit line, detailing dependent and independent variables, residuals, and simple versus multiple regression.
Support Vector Machine9:09
Explore the support vector machine, a supervised learning algorithm for classifying data using a maximum margin hyperplane and support vectors, with linear and nonlinear kernels.

Requirements

Not needed

Description

This course provides an in-depth introduction to basic and advanced topics in Artificial Intelligence (AI) focusing on planning and reasoning methods with the help of various AI techniques. Students will learn Linear Regression, a basic supervised learning method used for predicting output from input variables. The course includes Expert Systems, which simulate human expert decision-making capabilities through knowledge bases and inference engines, allowing for automated reasoning within complicated environments. One of the focuses is on Means-End Analysis, a goal-solving strategy that decomposes goals into sub-goals by determining the differences between desired and existing states. Goal Stack Planning, an AI approach utilizing stacks in handling and ordering actions depending on preconditions and existing goals, will also be learned by the students. Block World Problem is presented as a traditional planning problem to demonstrate search-based and logic-based planning methods. Planning in AI, including problem definition, choice of proper strategies, and execution of optimal plans in deterministic and probabilistic environments, will be derived from practical examples by students throughout the course. At the end of the course, students will be proficient in designing intelligent systems that support automated reasoning, prediction, and adaptive planning, providing a strong basis for further study or working in AI research and development.

Who this course is for:

Enthusiastic who need to learn AI