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Artificial Intelligence Practice Test: Inductive Logic Programming (ILP)
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Inductive logic programming (ILP) is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesised logic program which entails all the positive and none of the negative examples.

Schema: positive examples + negative examples + background knowledge ⇒ hypothesis.

Artificial Intelligence Practice Test: Inductive Logic Programming (ILP)
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10 Questions

1. Which of the following approach is used for refining a very general rule through ILP?
2. Which of the following is the total number of literals available in top-down inductive learning methods?
3. Which of the following need to be satisfied in inductive logic programming?
4. How many reasons are present for the popularity of ILP?
5. Which of the following technique is not useful for conveying relational knowledge?
6. Which of the following cannot be denoted by a set of attributes?
7. Which of the following produces hypotheses that are comfortable to read for humans?
8. Which of the following combines inductive methods with the power of first-order representations?
9. Which of the following is a suitable language for describing the relationships?
10. Which of the following inverts a whole resolution strategy?

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