Memorial University of Newfoundland COMP 3200
Professor Churchill · Memorial University of Newfoundland · 10 lectures with notes
Students in this class: ask your lecturer for the class code, and these lectures will already be in your library when you sign up.
COMP 3200 - Intro to Artificial Intelligence - Lecture 07 - Assignment 2
Dave Churchill explains how to implement and optimize A* pathfinding for variable-size grid objects in COMP 3200 Assignment 2.
COMP 3200 - Intro to Artificial Intelligence - Lecture 06 - Grid Representations and Vector Fields
Grid-based vector fields let large groups of entities share one efficiently computed route to a common goal.
COMP 3200 - Intro to Artificial Intelligence - Assignment 1 Tutorial
A practical guide to implementing and debugging Assignment 1’s BFS and DFS grid search in JavaScript.
COMP 3200 - Intro to Artificial Intelligence - Lecture 05 - Heuristic Search + A* Algorithm
A* combines path cost and estimated remaining cost to find optimal paths efficiently when its heuristic satisfies the right conditions.
COMP 3200 - Intro to Artificial Intelligence - Lecture 04 - Assignment 1
Assignment 1 uses BFS and DFS to teach both grid pathfinding and the JavaScript framework used for later assignments.
COMP 3200 - Intro to Artificial Intelligence - Lecture 03 - Problem Solving + Search Algorithms
Search algorithms share one framework; their fringe-selection strategy determines how they trade completeness, optimality, time, and memory.
COMP 3200 - Intro to Artificial Intelligence - Lecture 02 - Agents, Actions, and Environments
AI problem-solving starts by defining the agent, environment, actions, performance measure, and properties that constrain suitable algorithms.
COMP 3200 - Intro to Artificial Intelligence - Lecture 01 - Course Introduction
COMP 3200 teaches classical AI algorithms and independent problem-solving, with AI tools restricted to supporting—not replacing—students’ work.
COMP 3200 - Intro to Artificial Intelligence - Lecture 08 - Intro to Game Theory
Game theory predicts strategic choices by comparing payoffs, eliminating dominated strategies, and identifying mutual best responses.
COMP 3200 - Intro to Artificial Intelligence - Lecture 09 - MiniMax Search + AlphaBeta Pruning
Minimax models an opponent’s best response; alpha-beta pruning skips irrelevant branches without changing the search result.