- BRIEFLY define the following terms and give an example of how each term is used.
- Valid (in logic)
- Satisifiable (in logic)
- Unsatisfiable (in logic)
- Predicate
- Function (in logic)
- Propositional Logic
- Model in First-order Logic
- First-order Logic
- Unification
- Propositionalization
- Conjuctive Normal Form
- Horn Clause
- Universal quantifier
- Existential quantifier
- Resolution
- Forward Checking
- Backward Checking
- Situation Calculus
- Frame Problem
- Frame Axiom
- Planning
- Planning Action
- STRIPS operators
- Action Preconditions
- Action Effects
- Plan Goals
- Interference
- Threatening Actions
- Ordering Actions
- Inductive Learning
- Decision Tree
- Entropy
- Information Gain
- K-Nearest Neighbor
- Curse of Dimensionality
- Feature Normalization
- Artificial Neural Network
- Input Unit
- Hidden Unit
- Output Unit
- Linear Perceptron
- Thresholded Linear Perceptron
- Sigmoid Function
- Backpropagation
- Linearly separable

- Give algorithms for the following (in addition, for each algorithm indicate whether the search is complete, optimal and give some estimate of the cost of the algorithm):
- Forward Chaining in propositional logic
- Backward Chaining in propositional logic
- Resolution in propositional logic
- Forward Chaining in first-order logic
- Backward Chaining in first-order logic
- Resolution in first-order logic
- Decision tree construction using information gain
- K-nearest neighbor prediction
- Learning in a simple linear perceptron
- Backpropagation

- For each of the algorithms discussed above define a set of propositions/predicates and then show how the algorithm would work for those rules/facts.
- Define the rule Modus Ponens for propositional logic. Give an example. How does it differ for first-order logic?
- Repeat the previous question for Resolution.
- How would you convert a logical formula to Conjunctive Normal Form.
- How would you convert a logical formula to Horn Clauses.
- Explain how first-order logic differs from propositional logic. Give an example of the key parts of first-order logic.
- Why is First-Order Logic used over Propositional Logic?
- Look at samples of logical sentences in the text and convert them to first-order logic.
- How do the universal, existential quantifiers and negation relate?
- What is the frame problem? Why is it important in planning? What is a frame axiom? Give an example.
- Give two notions of negation in logic. Explain the advantages and disadvantages of each.
- What is unification? Why is it so important to first-order logic?
- How does planning differ from search? Give an example.
- How do STRIPS operators work? How are they used in planning?
- How does interference occur in planning? Give an example. What are some ways to avoid it?
- Decision trees are often chosen as a learning method because they are easy to understand and are complete in the set of hypotheses they can learn. What is meant by the latter?
- Artificial neural networks are said to be "inspired" by human brains? In what sense? Why do we focus on the human brain?
- What is the importance of the sigmoid function in backpropagation learning?
- Many learning methods such as the k-nearest neighbor method rely on normalization to be effective. Explain why this is and give an example demonstrating why normalization would be needed.