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 - 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: 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 predicate logic). Agent paradigms: the hierarchical paradigm, the reactive paradigm, and the hybrid paradigm. Classical planning and execution, STRIPS, Shakey. 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.