InfoCoBuild

CPSC 322: Introduction to Artificial Intelligence

CPSC 322: Introduction to Artificial Intelligence (2013, University of British Columbia). Instructor: Professor Alan Mackworth. This course provides an introduction to the field of artificial intelligence. The major topics covered will include reasoning and representation, search, constraint satisfaction problems, planning, logic, reasoning under uncertainty, and planning under uncertainty.

Lecture 15 - Local Search for Constraint Satisfaction Problems


Go to the Course Home or watch other lectures:

Lecture 01 - What is Artificial Intelligence?
Lecture 02 - Representational Dimensions
Lecture 03 - Applications of Artificial Intelligence
Lecture 04 - Representation & Search Framework
Lecture 05 - Breadth-first Search (BFS) and Depth-first Search (DFS)
Lecture 06 - Search with Costs & Heuristic Search
Lecture 07 - Heuristic Search: A*
Lecture 08 - A* Optimality, Cycle Checking
Lecture 09 - Iterative Deepening (IDS) and IDA*
Lecture 10 - Multiple Path Pruning, IDS and IDA*
Lecture 11 - Branch & Bound and Constraint Satisfaction Problems (CSPs)
Lecture 12 - Solving Constraint Satisfaction Problems using Search
Lecture 13 - Arc Consistency in Constraint Satisfaction Problems
Lecture 14 - Generalized Arc-Consistency (GAC) Algorithm and Domain Splitting for CSPs
Lecture 15 - Local Search for Constraint Satisfaction Problems
Lecture 16 - Stochastic Local Search
Lecture 17 - Stochastic Local Search Algorithms
Lecture 18 - Planning: Representation
Lecture 19 - Forward Planning and CSP Planning
Lecture 20 - CSP Planning Wrap Up
Lecture 21 - Intro & Propositional Definite Clause Logic
Lecture 22 - Logic: Proof Procedures, Soundness and Completeness
Lecture 23 - Logic: Bottom-up and Top-down Proof Procedures
Lecture 24 - Logic: Top-Down Procedure, Datalog and Big Picture
Lecture 25 - Probability Theory: Intro
Lecture 26 - Conditional Probability, Bayes Rule, Chain Rule
Lecture 27 - Independence
Lecture 28 - Bayesian Networks Intro
Lecture 29 - Independence and Inference
Lecture 30 - Variable Elimination for Bayes Nets
Lecture 31 - Uncertainty Wrap-up, Decision Theory: Single Decisions
Lecture 32 - Decision Theory: Single and Sequential Decisions
Lecture 33 - Decision Theory: Optimal Policies for Sequential Decisions
Lecture 34 - Perspectives and Final Review