In each lecture where there is new reading material, each student must create one “thought question” about the material. This question must be related to the reading and be thought-provoking. The question must also come with some answers. The more answers the better. If you manage to create a question regarding the topic you receive one point for the question. If it is thought-provoking or one can see that you spend some thought on it, you receive 2 points. So there are 2 points available each time you are supposed to hand in thought questions, giving you three possible grades: 0, 5 and 10. You should also show up in class with your thought questions as they will be used if there is some time left for discussion. Then a student will be selected randomly and should then read one of his/her questions to spark some discussion in class.
Every time there is some new reading material a project will appear in MySchool where you can hand your thought questions in.
Let´s take a look at some example thought questions from chapter 2(you will NOT hand in thought questions for chapter 2).
Examples of bad thought questions:
Q: What is an agent?
A: An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators.
Q: What types of agents are there:
A: Simple reflex, model-based, goal-based and utility-based
Examples of good thought questions:
Q: Should the critic element of a learning agent be static or dynamic, meaning that it could change over time?
A: It should be static so that all the experience that has been gathered doesn't become invalid.
A: It should be dynamic because if the environment is dynamic, the critic should be able to adjust to it
A: It should only change if its goals change.
Q: How can learning improve the types of agents mentioned in the chapter.
A: Simple reflex agents could learn new if-then rules or change its own. Model-based agents could use learning to update their models. Goal-based agents could use learning to improve the plan search. Utility-based agents can use learning to improve its utility function
Q: Is it good for the programmer to know the environment which his/her agent will operate within?
A: Yes, because it will create a better agent.
A: No, because there is a danger of the agent's design being too dependent on the environment and its attributes.