Type of course

Admission requirements

Course content

The course gives basic knowledge of methods, strategies and application areas in Artificial Intelligence (AI). The focus is on AI for:

• decision-support and knowledge-based systems,
• agent systems with software agents and intelligent agents including learning methods for agents (e.g., decision-trees, machine learning, neural networks, reinforcement learning and deep learning).
• Focus is on search strategies, planning and scheduling, as well as information retrieval.

Relevance in study program: artificial intelligence as a subject and technology is a part as significant application in the other scientific subjects.

Programming tasks in the course requirements will solve relevant problems in artificial intelligence.

Recommended prerequisites

Objectives of the course

Knowledge - students have:

• basic knowledge of AI problems and solutions including search algorithm and AI planning and optimization for agent systems.
• knowledge of knowledge representation forms and knowledge bases including justification strategies, decision support and heuristics.
• basic knowledge of multi-agent systems and its methods and strategies including software agents, intelligent agents, meta-agents and mobile agents.
• knowledge of agent systems that use machine learning, neural networks and deep learning
• knowledge of applications for intelligent systems and multi-agent systems with intelligent agents and other agents such as:
  • decision support systems and knowledge-based systems that can be used for intelligent agents in decisive decision-making systems
  • information retrieval
  • cyber-physical system and robotics.

Skills - students can:

• use methods and techniques for KI applications including algorithm and optimization, and develop applications within intelligent systems and agent systems.
• use search algorithms, AI planner and planning, knowledge representation forms and reasoning strategies, decision support and heuristics.
• develop intelligent systems, mobile intelligent agents and multi-agent systems including software agents, and various intelligent agents as well as cyber-physical systems and robotics.
• develop intelligent systems, mobile intelligent agents and multi-agent systems using machine learning, neural networks and deep learning
• handle the most common algorithms in machine learning and neural networks, deep learning for intelligent systems and multi-agent systems
• formulate and carry out a well-defined and qualified KI project with a focus on intelligent systems and agent systems.

General competence - students can:

• present basic problem and challenges within intelligent systems and multi-agent systems.
• identify the requirements for different KI systems and KI applications within intelligent systems and multi-agent systems including decision support and knowledge-based systems,
• use AI methods, techniques, and applications and develop intelligent systems and multi-agent systems including decision support and knowledge-based systems, cyber-physical systems and robotics
• use and develop intelligent systems and multi-agent systems that use algorithms in machine learning, neural networks and deep learning.
• present in depth and discuss how KI influences the design and realization of AI systems with a focus on intelligent systems and multi-agent systems
• develop a AI prototype that solves a AI problem where intelligent systems and multi-agent systems with decision support with knowledge bases are used, and / or with algorithms within machine learning, neural networks and deep learning.

Language of instruction and examination

Teaching methods

Information to incoming exchange students

This course is available for inbound exchange students.

This course has recommended academic prerequisites. Please see the «Prerequisite» section for more information.

Do you have questions about this module? Please check the following website to contact the course coordinator for exchange students at the faculty: INBOUND STUDENT MOBILITY: COURSE COORDINATORS AT THE FACULTIES | UiT

Recommended reading/syllabus