Problem Solving and Search in Artificial Intelligence - Lecture 3

Overview

Explore problem-solving and search techniques in artificial intelligence through this comprehensive lecture. Dive into problem-solving agents, goal formulation, and problem definition before examining various search strategies including breadth-first, uniform cost, depth-first, depth-limited, and iterative deepening depth-first search. Learn about search trees, the fringe (open list), and how to avoid repeated states using a closed list. Gain insights into performance considerations and understand the differences between tree search and graph search algorithms. Conclude with practical applications through assignment algorithm pseudocode.

Syllabus

- Problem Solving Agents
- Example Problem
- Goal Formulation
- Problem Definition
- Paths and Costs
- Example Graph Problem
- What is Search?
- The Search Tree
- Sliding Tile Puzzle
- Which Node to Expand?
- Search Node Data
- Node vs State
- The Fringe Open List
- General Uninformed Tree Search
- Expand Function
- Problem Solving Performance
- Recap / Exam Questions
- Search Strategies
- Breadth-First Search BFS
- Uniform Cost Search UCS
- Depth-First Search DFS
- Depth-Limited Search DLS
- Iterative Deepening Depth-First Search ID-DFS
- Recap of Performance
- Avoiding Repeated States Closed List
- General Graph Search with Closed List
- Assignment 1 Algorithm Pseudocode
- "Tree Search" vs "Graph Search"