Artificial Intelligence – Exercise Set

Exercise 1 — Definitions of AI

Using the definitions of Artificial Intelligence presented in the lecture:

  1. Select three different definitions of AI from the lecture and explain how they differ in focus.
  2. Provide one real‑world example that fits each definition.

Exercise 2 — Turing Test and Human‑like Behavior

Based on the Turing Test section:

  1. Explain the Turing Test in your own words.
  2. List the four cognitive tasks required for human‑level performance.
  3. Give an example of a modern system that demonstrates one of these tasks.

Exercise 3 — Learning Methods

The lecture describes imitation, supervised training, and reinforcement learning.

  1. Define each learning method.
  2. Provide a real example of each.
  3. Explain which method is most suitable for autonomous robots and why.

Exercise 4 — Genetic Algorithms

Using the genetic algorithm description:

  1. Describe the structure of a chromosome and a gene.
  2. Explain how new offspring are generated.
  3. Describe the role of probabilistic selection.
  4. Give one example of a problem solvable by genetic algorithms.

Exercise 5 — Artificial Neural Networks

Based on the neural network diagrams and explanations:

  1. Explain how an artificial neuron computes its output.
  2. Describe the role of weights and thresholds.
  3. Create a small example with three inputs and compute the output.
  4. Explain how networks are trained using feedback.

Exercise 6 — Robotics and AI Challenges

Using the robotics section of the lecture:

  1. Explain why perception and reasoning are essential for autonomous robots.
  2. Describe the difference between plan‑based and reactive behavior.
  3. Provide one example of evolutionary robotics.
  4. Discuss one ethical or societal issue raised by AI.