The final exam will be a 3-hour open-book on-line exam (Canvas).

Questions will focus on your understanding of the material that has been presented and discussed throughout the course and that which can be found in the assigned readings and lecture notes, with heavy emphasis on the main concepts and topics, and your ability to holistically comprehend these.

If you are unsure of which topics are the main ones, and which are sub-topics or less important, look at the lecture notes: If it's mentioned there it is important. If it's mentioned more than once it's even more important. But above all, your comprehension of the relationships between topics, and your ability to put this in context with the present state and future of AI, is what the final exam focuses on.

Below are some example questions. Please note that this is not an exhaustive list of the types of questions that may appear on the final exam, it is representative form/version of some questions that may appear on the final exam, and are solely provided to help you prepare for the final exam. These are examples only, the specific question below may (or may not) appear - in this or modified form - on your final exam.

However you answer and whatever you write, make sure you present strong and clear arguments for your answer, referring back to the most relevant material covered in the course, as appropriate.

1. What is abduction and why is it more complicated in non-axiomatic worlds than in axiomatic ones?
2. How does learning through reasoning work?
3. Is empirical reasoning the same as axiomatic reasoning? Why / why not?
4. In what ways do the mechanisms of a rule-governed world that a learning agent inhabits affect its learning process?
5. What is the relation between knowledge, information, data, and measurement?
6. What are two examples of reasoning methods included in the set called “ampliative” reasoning?

The answers are neither exhaustive nor complete. The following answers are partial and only provided as a hint to the full/complete answers (which are missing). Complete answers can be relatively easily constructed by studying the lecture notes and reading the assigned papers.

1. 'Abduction' is one kind of reasoning that can be used to handle missing knowledge. In non-axiomatic worlds the rules of the world can never be acquired with certainty and thus conclusions cannot be deemed 'true' or 'false' with absolute certainty.
2. Reasoning methods are used to infer missing information that is used to model (describe the dynamics of) particular mechanisms in the world.
3. No, it isn't.
4. They define the limits of what can and cannot happen; they harbor the (hidden) mechanisms that the learning must model.
5. Measurement produces data; data becomes 'information' when put in particular contexts or used for particular purposes; information is knowledge when it is general enough to be used for several purposes.
6. Abduction, deduction.

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