Foundations of Artificial Intelligence I: Introduction to AI, Agents

Foundations of Artificial Intelligence I: Introduction to AI, Agents is an introductory-level course, created by the German Research Centre for Artificial Intelligence (DFKI) through the KI-Campus learning platform for Artificial Intelligence (AI), which aims to equip participants with a foundational knowledge of AI as a research field. KI-Campus, or the German AI Campus is a research and development (R&D) project funded by the German Federal Ministry of Education and Research (BMBF). The learning platform has been available to the public in its beta version since 2020 and is in continuous development, offering trainings and upskilling opportunities in English and German. DFKI is Germany's leading non-profit public-private partnership for software technology innovation in AI methods. 

The Foundations of Artificial Intelligence I is part of the course series 'Foundations of Artificial Intelligence I-IV', a collection of individual courses dealing with different AI methodologies, which can be completed separately from one another. The first course, Introduction to AI, Agents, focuses on the intersection of AI and research and introduces the concept of the rational agent (a term used in economics, game theory, and AI to denote decision-making and agency). A rational agent is any entity, capable of making decisions - whether this is a human, company, machine or a software application. Themes and topics covered by the course include: the Turing test and its significance to the development of AI as a research field, the history and evolution of the technology, and different phases of advancement brought about by AI. 

Through a combination of discussion, self-reflection and asynchronous learning activities, participants are also invited to consider future developments in the sector and develop a deeper understanding of the limitations, challenges and opportunities of AI and its future application. Learners are also introduced to the different types of rational agents and their underlying architecture and touch upon 'hot topics' in AI research like transfer (or supervised) learning and reinforcement learning (or the development of self-teaching agents to solve a problem).