Course content

Path finding

FSM

Scripts

Symbolic AI Techniques

Logic

Multi agent systems

State based search

Goal directed search

Genetic Algorithms / Programming

Neural networks

Reinforcement learning

Learning outcome

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

Understand and evaluate various core techniques and algorithms of AI, namely agent technology, informed and uninformed tree and graph search algorithms, various learning techniques including artificial neural networks, decision tree learning and evolutionary algorithms, logic and planning techniques and algorithms, knowledge representation, the meaning of concepts such as intelligence, 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 mobile robots. The students will also enhance their programming skills in a preferred language of their own and in Java by learning to program a mobile robot.

Improve programming skills through the programming of mobile robots. Programming mobile robots help with connecting the theory learnt in class with the practical use of it.

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

Learning methods and activities

Forelesninger

Oppgaveløsning

Utfyllende informasjon:

This course will focus on 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

IMT2021 Algorithmic Methods

Required previous knowledge

IMT1031 Fundamental Programming

Course materials

Artificial Intelligence: A Modern Approach, 3rd Edition by Stuart Russell and Peter Norvig, 2010