Introduction to Artificial Intelligence: Problem Solving and Search - Lecture 3

Learn about problem-solving agents and search algorithms in artificial intelligence through a comprehensive lecture that covers fundamental concepts and techniques. Explore goal formulation, problem formulation, and various search strategies including Breadth First Search (BFS), Uniform Cost Search (UCS), Depth First Search (DFS), Depth Limited Search (DLS), and Iterative Deepening DFS (IDDFS). Master the differences between tree search and graph search, understand node expansion and the fringe concept, and analyze problem-solving performance and complexity. Examine practical implementations through example scenarios and graph problems, while gaining insights into avoiding repeated states and utilizing closed lists. The lecture concludes with assignment-related pseudocode discussion and a thorough recap with exam preparation guidance.

Syllabus

- Introduction
- Problem Solving Agents
- Example Scenario
- Goal Formulation
- Problem Formulation
- Solution / Path
- Example Graph Problem
- What is Search
- Search Tree / Procedure
- Node Data / Node vs State
- The Fringe open list
- General Uninformed Tree Search
- Node Expansion
- Problem Solving Performance / Complexity
- Search Strategies
- Breadth First Search BFS
- Uniform Cost Search UCS
- Depth First Search DFS
- Depth Limited Search DLS
- Iterative Deepening DFS IDDFS
- Comparing Algorithms
- Avoiding Repeated States
- Closed List / Graph Search
- Assignment 1 Pseudocode
- Tree Search vs Graph Search
- Recap / Exam Questions / Questions