Artificial Intelligence

The course starts with a description of problem solving methods by means of heuristic search. Thereafter, various knowledge representation languages and inference methods for automatic problem solving. Representation in form of predicate logic, frames and semantic nets are treated, and connected to the main forms of reasoning - especially rule based reasoning. Furthermore, architectures that integrates various reasoning methods, agent based architectures and architectures for interactive problem solving. Numerous application examples are given to demonstrate the methods.

On successful completion of the module, students will be able to:

* Understand and evaluate various core techniques and algorithms of AI, including machine learning, tree and graph search algorithms, Markov decision process, Constraint satisfaction problems, and Bayesian networks. Understand the meaning of concepts such as intelligence, agents, reasoning, and making inferences.

* Identify different uses and applications of AI techniques and algorithms, from neuroscience, understanding brain to game development, to web technologies and secure system designs.

* Implement several of the algorithms on the coding games and different AI problems. The students will also enhance their programming skills in a preferred language of their own by learning to program AI algorithms and/or agents.

* Improve programming skills through the programming of AI algorithms and/or intelligent agents. Programming exercises and assignments help enhancing the understanding the theory learnt in class.

* Evaluate the run-time and memory complexity of several AI algorithms, and practice with creating better algorithms.

Lectures, exercises, self-study and obligatory assignments.

This course will focus on the practical implementation of AI concepts. Lectures will introduce a topic area, and students are expected to implement and report on the key concept.