Planning with Artificial Intelligence

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.