Logic and AI Concepts

Who is the most handsome lecturer that is teaching COS30019 in Semester 2, 2024?

• Dr Joel Than

• Dr Kelvin Yong

• Dr Colin Tan

• Dr Vong Wan Tze

What is propositional logic?

• Propositional logic is a formal system in which statements are represented as propositions that can be true or false.

• Propositional logic is a type of programming language.

• Propositional logic is a method for solving equations.

• Propositional logic deals only with numerical data.

Define first order logic.

• First order logic is a type of programming language.

• First order logic is a formal system that uses quantifiers and predicates to express statements about objects and their relationships.

• First order logic is a method for solving equations.

• First order logic only deals with numerical values.

What is the main difference between propositional and first order logic?

• Propositional logic includes predicates, while first order logic does not.

• First order logic includes quantifiers and predicates, while propositional logic does not.

• Propositional logic uses quantifiers, while first order logic does not.

• First order logic is simpler than propositional logic.

Explain the concept of AI planning.

• AI planning is the process of generating a sequence of actions to achieve specific goals.

• AI planning involves random decision-making without goals.

• AI planning is solely about data storage.

• AI planning is the same as machine learning.

What are the key components of a planning problem in AI?

• Key components include initial state, goal state, actions, and constraints.

• Actions, outcomes, resources, and timelines.

• Initial state, final state, rewards, and feedback.

• Constraints, objectives, environments, and agents.

Differentiate between supervised and unsupervised learning.

• Supervised learning is faster than unsupervised learning.

• Unsupervised learning requires more data than supervised learning.

• Supervised learning is only used for classification tasks.

• Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data to find patterns.

What is deep learning and how does it relate to machine learning?

• Deep learning is a type of traditional programming.

• Deep learning is a subset of machine learning that uses deep neural networks to model complex data patterns.

• Machine learning is a subset of deep learning.

• Deep learning only works with structured data.

What is overfitting in machine learning?

• Overfitting is when a model performs equally well on both training and unseen data.

• Overfitting refers to a model that is trained on too little data, leading to poor performance.

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns in the data.

• Overfitting is when a model performs well on training data but poorly on unseen data due to excessive complexity.

How can you prevent overfitting in a model?

• Use techniques like regularization, cross-validation, and dropout.

• Ignore validation metrics

• Increase the model complexity

• Use a single training dataset