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.\\

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Remember:

    * Give answers to your questions. I received some very good questions but no answers to them. I can´t give a full mark for questions without answers.
    * Give more than one answer. This is a good way to show "the quality of your thought" as you need to look at the problem from more than one side.
    * The questions don´t need to have complete answers. If you can´t really answer your question you might be on the right track :)
    * Try imagining that your sitting in a café with some of your peers from the class. How would you pose an interesting question from the text you just read
    * The questions don´t need be straight from the text. Maybe you could form the question around what you could do with the technology discussed in the text