This is an old website! For the current version, visit: http://www.cse.chalmers.se/edu/course/TIN172/
This is a joint Chalmers/GU course. It has two different course codes and two different course plans, but in reality it is exactly the same course:
The goals of this course are to:
This year we have changed the main course book:
David Poole and Alan Mackworth (2010). Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press.
The main reason for changing is that this book is freely avaliable in HTML format, and there are also lot of associated programs, applets and lecture slides:
Of course, you can still buy the printed book at Cremona. The previous course book is still a very good AI book, and is a great complement to the online material:
Stuart Russell and Peter Norvig (2002, 2010). Artificial Intelligence: A Modern Approach. Pearson.
For lecture slides and reading instructions in the course book, see the schedule.
This is an advanced course: we assume academic maturity, and a willingness to explore independently and good programming skills.
You should be a good programmer (in any suitable programming language), and have enough experience in programming to do a sizeable project. You must also be fluent in the use of tools, libraries, environments, etc. used with your programming language. We will not be able to help you with your coding problems. Your programs will be discussed at an abstract, pseudo-code level.
We assume that you have participated in a number of programming courses including a course about datastructures and basic algorithms or that you have acquired corresponding knowledge by yourself.
If you are taking the course, there are some rules that you have to follow.
Keep up
Help us to do necessary administration
Form a team
Respect deadlines
The FAQ of usenet’s comp.ai group starts by saying (we paraphrase):
Artificial intelligence (“AI”) can mean many things to many people. Much confusion arises because the word “intelligence” is ill-defined.
“Strong AI”, a term that itself has several definitions, can mean the bold claim that computers can be made to think on a level (at least) equal to humans. More relevant to us is the claim that a sufficiently intelligent machine can be conscious and experience emotions, etc., as we humans do. This course does not concern itself with such questions.
Instead, this course does “weak AI”, which simply means adding some “thinking-like” features to computers to make them more useful tools. What does “thinking-like” mean? A working definition is “not obviously machine-like”, an obviously elastic category that shrinks as your experience and expectations grow. The more you understand how an AI program works, the less intelligent it appears!
Our distinction between strong and weak AI can be explained by an analogy. We conquered the air by building planes, not by studying birds, though the latter is an interesting study in its own right. So: cognitive psychology is fascinating, but we build more useful machines simply by trying out different programming techniques.
Our focus on weak AI does not mean that impressive achievements are impossible. With current “thinking-like” features, machines are able to perform, for example, the following tasks:
In this course, students will learn the most important techniques underlying these and other “intelligent” achievements via a concrete course project.