Artificial Intelligence Principles, and Practices Part I

About this course

Introduction to Artificial Intelligence- The fundamental concepts, principles and practices.: Intelligent Agents – Agents and environments – PEAS Performance Parameters, Environment, Actuators, Sensors. Good behavior – The nature of environments – The structure of agents - Problem-Solving agents – How to define a problem? Problem Definition – State Space, Initial State, Goal State, Goal Test, Transition Model, Actions, Sensors. Acting under uncertainty – The 8-Puzzle problem , The 8-Queens problem. The Wumpus World problem-Partially Observable Space - Inference using full joint distributions; –Independence; Bayes’ rule and its use; –The Wumpus world revisited. Searching Techniques: Tree Search Algorithm and Graph Search Algorithm, Redundant path, Loopy Path - Problem-Solving Agents, Well-defined problems and solutions, Formulating problems, Real-world problems. Uninformed Search Strategies, Breadth-first search, Start from Initial State, Choose the data structures Frontier and Explored set. Uniform-cost search with Priority Queue with the cost function, Depth-first search, Last In First Out Queue - Depth-limited search, Iterative deepening depth-first search, Bidirectional search, Informed (Heuristic) Search Strategies, Greedy best-first search, A* search: Minimizing the total estimated solution cost, Heuristic Functions. The effect of heuristic accuracy on performance. Beyond Classical Search, Local Search Algorithms, Hill Climbing Algorithm, Stochastic Hill Climbing Algorithm. Optimization Problems, Local Search in Continuous Spaces, Local Beam Search, Genetic Algorithm, Example of Gentic Algorithm for 8-Queens problem.

Course objectives

• Introduce students to the fundamentals of artificial intelligence.
• Develop problem-solving skills through structured frameworks.
• Enable students to analyze and implement search algorithms effectively.