Fundamentals of Artificial Intelligence

About the course

The course provides a basic introduction to classical AI (artificial intelligence) as well as non-classical AI. It addresses fundamental conditions, problems and challenges for AI also from a philosophical perspective.

Topics covered:

• Background and history of AI in outline.
• Fundamental problems and challenges, e.g. - realism, brittleness, scalability, real-time requirements, the frame problem, the homunculus problem, the substrate problem, symbol grounding, common-sense knowledge and common-sense reasoning.
• Fundamentals of search, e.g. problem, solution, state space, breadth-first, depth-first, heuristics, A*, local search and optimization.
• Knowledge representation: logic as form of expression (syntax and semantics of propositional logic and first-order logic).
• Agent paradigms: the hierarchical paradigm, the reactive paradigm, and the hybrid paradigm. Classical planning and execution.
• Reactive agents, Braitenberg vehicles, subsumption architecture. Potential fields architecture.
• The physical structure of robots.
• Teleoperation and semi-autonomous robots.
• Embodied cognition and situatedness.
• Neural networks: background and fundamentals.
• Artificial evolution, genetic algorithms - short introduction.
• Multiple autonomous agents, swarm intelligence, stigmergy, emergence.
• Learning - short introduction.