Aims:
Study how to use computers to simulate human intelligence activities such as: perception, reasoning, learning, thinking, and planning
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisites: none.
Location: ALP.
Semester: 1.
Syllabus:
This course is mainly divided into two parts: theoretical teaching and practical teaching.
Theoretical Teaching
The development history and latest progress of artificial intelligence.
Thoughts and characteristics of various search strategies.
The minimax search of game trees and the pruning strategy of α-β.
The minimax search and pruning process analysis of tic-tac-toe.
Practical Teaching
The optimal solution for the "sliding block" game.
Design the "Connect6" game program.
Learning Outcomes: Subject Mastery
Explore several philosophical and ethical issues related to artificial intelligence.
Understand and master formal methods and techniques such as problem description, problem modeling and search space analysis.
Select the optimal data structure and algorithm for subprocesses.
Design the heuristic function of sliding blocks.
Inductive chess shape.
Visual search method of sliding block problem.
Performance test report.
Learning Outcomes: Personal Abilities
Ability to do complex programming.
SCQF Level: 9.
Credits: 15.