Game-playing AI with Swift for TensorFlow (S4TF) Cognitive Class Exam Quiz Answers

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Enroll Here: Game-playing AI with Swift for TensorFlow (S4TF) Cognitive Class Exam Quiz Answers

Game-playing AI with Swift for TensorFlow (S4TF) Cognitive Class Certification Answers

Module 1 – Set Up Your Environment Quiz Answers – Cognitive Class

Question 1: What kind of IBM Cloud account doesn’t require payment information and doesn’t expire?

Trial
Lite
Pay-as-you-go

Question 2: Which of the following platforms does Swift NOT run natively on?

Linux
Darwin
Windows
None of the above

Module 2 – Understanding Swift’s Basic Quiz Answers – Cognitive Class

Question 1: Which of the following companies did Chris Lattner work at after Apple?

IBM
Microsoft
Google
Amazon
Tesla

Question 2: For how many years was Swift in development before it was released to the public?

4 years

Question 3: What kind of programming language is Swift?

Interpreted
Compiled to machine code
Compiled to byte code
Transpiled

Question 4: Which HTTP server for Swift will this course use?

Perfect
Kitura
Swifter
Vapor

Module 3 – Tic Tac Toc (Minimax) Quiz Answers – Cognitive Class

Question 1: Why does our implementation of Minimax keep track of the depth of the tree?

Stop at a certain depth
Memoize game states
Take into account the “straightforwardness” of moves

Question 2: How is the tic-tac-toe board stored?

Row-major format
Column-major format

Question 3: If minimax started from a blank board and had to calculate the best possible move, how many board states would it evaluate? For the sake of mathematical simplicity, assume the board must be filled completely to be considered “over” – players cannot win.

9!
9^2
9^9
9*9

Question 4: The “minimax” function returns the best move to take for a board state.

True
False

Module 4 – Cartpole (Reinforce Learning) Quiz Answers – Cognitive Class

Question 1: The cartpole game is what kind of problem?

Inverted Pendulum

Question 2: The @differentiable function decorator is an example of an implementation of what technology/technique?

Question 3: Why do we only train the network with the top 30% of episodes?

To reduce training time with fewer samples
To improve network performance with better samples

Question 4: OpenAI Gym…

Provides easy-to-use game enviroments to test RL agents
Ships with RL algorithms to use as agents

Note: Make sure you select all of the correct options—there may be more than one!

Module 5 – 2048 (Monte Carlo Tree Search) Quiz Answers – Cognitive Class

Question 1: The implementation of the 2048 game logic is made fast by:

Using bitboards
Pre-calculating moves and scores for rows
Multi-threading

Note: Make sure you select all of the correct options—there may be more than one!

Question 2: Which framework did we use to enable Swift to host HTTP servers?

Kitura

Question 3: Monte Carlo Tree Search is…

Stochastic
Guaranteed to always give the correct answer

Note: Make sure you select all of the correct options—there may be more than one!

Question 4: Which swift file defines dependencies and other package details?

Package.swift

Game-playing AI with Swift for Tensor Flow (S4TF) Final Exam Answers – Cognitive Class

Question 1: Which tier(s) of IBM Cloud require payment information on file?

Lite
Trial
Pay-as-you-go

Question 2: Why would you want to use a single language, Swift, over many languages, each specialized for a certain task?

It’s easier to maintain a codebase written in a single language.
Swift is an expressive, performant, and open-source language backed by a large company (Apple).
It’s slow to facilitate communication between components in different languages.
It reduces the amount of “reinventing the wheel” required across a codebase.

Question 3: Does Minimax plays perfectly all the time? If so, why?

It’s trained on a lot of game data and learns how to play perfectly.
It brute forces a solution based off of the rules of the game, and all possible future situations.
It’s provided optimal heuristics.
It doesn’t always play perfectly.

Question 4: Classes are passed by reference, and structs are always, indiscriminately passed by value.

True
False

Question 5: A computed property is…

A variable within a struct/class/enum.
A function within a struct/class/enum that’s accessed like a property.

Question 6: Why is Minimax penalized for looking at moves deeper into the game tree?

To improve performance by looking at a more shallow game tree.
So Minimax takes into account how straightforward a move is.
To make Minimax more accurate.

Note: Make sure you select all of the correct options—there may be more than one!

Question 7: Why is Reinforcement Learning important?

It’s more accurate than other training methods.
It can learn by trial and error.
It doesn’t require as much data to learn from.
It can play games.

Note: Make sure you select all of the correct options—there may be more than one!

Question 8: Swift for TensorFlow is interoperable with Python, because Python’s written in C

True
False

Question 9: What are some reasons Swift for TensorFlow is special?

Swift now has a wrapper around TensorFlow, enabling machine learning development.
Swift for TensorFlow can automatically differentiate complex functions.
Swift for TensorFlow can optimize complex tensor operations.

Note: Make sure you select all of the correct options—there may be more than one!

Question 10: For what reasons did we implement monte carlo tree search in a time-bounded manner?

To reduce the amount of time the algorithm takes to search.
To search game states more if they’re closer to the end of a game.
To make MCTS more accurate.
To distribute the algorithm across threads.

Introduction to Game-playing AI with Swift for TensorFlow (S4TF)

Game-playing AI refers to the development of artificial intelligence agents that can play and learn from games. This can include traditional board games, video games, and other interactive environments. Swift for TensorFlow can be used to build game-playing AI models, leveraging the capabilities of TensorFlow for machine learning and deep learning tasks.

Here are the general steps you might follow to create a game-playing AI with Swift for TensorFlow:

Integration of TensorFlow with Swift: Import TensorFlow into your Swift project using Swift for TensorFlow. This allows you to leverage TensorFlow’s capabilities for building and training machine learning models.
Data Preparation: If your game requires training data, you’ll need to prepare a dataset that includes input features and corresponding labels. For reinforcement learning scenarios, this may involve collecting data from interactions with the game environment.
Model Definition: Define a neural network model using Swift for TensorFlow. This involves specifying the architecture of your model, including the layers and activation functions.
Training the Model: Use TensorFlow to train your model on the prepared dataset. This might involve techniques like supervised learning for labeled data or reinforcement learning for scenarios where the AI agent interacts with the game environment to learn optimal strategies.
Integration with Game Environment: Connect your trained model with the game environment, allowing it to make decisions and take actions based on its learned knowledge.
Evaluation and Fine-tuning: Evaluate the performance of your AI agent in the game environment. Fine-tune the model if needed to improve its performance.