The course "Foundations of Artificial Intelligence I" introduces into the field of artificial intelligence and one of its most important metaphors, which is metaphor of the rational agent. 

It discusses the beginnings of artificial intelligence with Alan Turing and his seminal article where he asked the question whether machines can think, touches upon the notions of symbolic and subsymbolic AI and discusses four possible directions of AI research. We review the Turing test as a test of intelligence and discuss a working definition for artificial intelligence. An overview over the history of AI, a discussion of selected current challenges of AI, and an outlook on the next decades of AI research conclude the first module. 

The second module focuses on the metaphor of the rational agent. It looks closely at the properties of agents and environments and discusses how agents can act successfully in different environments. Different types of agents and their underlying architectures are discussed. 

The course is based on the textbook by Stuart Russell and Peter Norvig: Introduction to Artificial Intelligence - A Modern Approach, 3rd edition, 2012, or 4th edition 2020. The 3rd edition is also available in German: Stuart Russell und Peter Norvig: Künstliche Intelligenz - Ein Moderner Ansatz, 3. aktualisierte Auflage, Pearson 2012.

"Foundations of Artificial Intelligence I" is part of the course series "Foundations of Artificial Intelligence" that covers a variety of algorithms and methods that are of central importance in AI and of major practical relevance.

The course is held in English language with German subtitles.

Module Introduction

• The beginnings of Artificial Intelligence
  • Alan Turing and his seminal article «Can machines think?
  • Symbolic and Subsymbolic AI
  • Four possible directions of AI research
  • The Turing Test
  • A working definition for «Artificial Intelligence»
• History of AI
  • The beginnings of AI (1943 - 1956)
  • Early successes and enthusiasm (1956 – 1969)
  • Steady progress with a dose of reality (1970 – 1985)
  • The AI winter (1987–1993)
  • Data-driven AI (since 2000)
• Selected current challenges of AI and the next decades of research
  • Transfer and reinforcement learning
  • Creativity and „magic“ during AI-based learning
  • Limitations in the understanding of unstructured information
  • Bias in data and automatic decisions
  • Ethical questions
  • The 4 big A of AI search and the US AI community roadmap

Module Intelligent Agents

• Metaphor of the rational agent
  • The Rational Agent
  • Modeling an Agent
  • Performance Measure and Utility Function
• Properties of Agents and Environments
  • The PEAS Description of an agent/environment system
  • The Utility Function of an Agent
  • Utility vs. Performance
  • Properties of Environments
  • Simple and Complex Environments
• Architectures of intelligent agents
  • Simple Reflex agent
  • Model-based Reflex agent
  • Goal-based agent
  • Utility-based agent
  • Learning agent

By the end of the course, you‘ll be able to...

• reflect on the history of artificial intelligence and its current challenges,
• understand the basic design of intelligent systems based on the concept of the rational agent,
• participate in AI projects in practice.

None.