Course content

The subject starts with a description of problem solving methods by means of heuristic search. Therafter, 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 resoning methods, agent based architectures and architectures for interactive problem solving. Numerous applicaton examples are given to demonstrate the methods.

Learning outcome

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.

Learning methods and activities

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.

Specific conditions

Recommended previous knowledge

BMA1010 or IMAG2021
IDATG 2102

Required previous knowledge

IMT2021 Algoritmiske metoder

REA1101 Matematikk for informatikkfag eller REA2091

Course materials

To be announced.

May include:
- History and overview of AI
- Machine learning
- Search
- Markov decision process
- Game playing
- Constraint satisfaction problems
- Bayesian networks
- Other AI topics