INF-2600 Artificial Intelligence, AI - Methods and applications - 10 stp

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.

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.