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

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

Game-playing AI with Swift for TensorFlow (S4TF) can be a fascinating area to explore, combining the power of Swift’s expressive syntax with TensorFlow’s robust machine learning capabilities. Here’s an introduction to getting started with building game-playing AI using S4TF:

What is Swift for TensorFlow (S4TF)?

Swift for TensorFlow (S4TF) is a framework developed by Google that allows developers to leverage the Swift programming language for machine learning tasks, integrating seamlessly with TensorFlow. Swift is known for its safety, speed, and expressiveness, making it an excellent choice for developing AI applications.

Setting Up Swift for TensorFlow (S4TF)

Before diving into game-playing AI, you’ll need to set up S4TF on your machine:

1. Install Swift: Install Swift on your system. You can download pre-built toolchains from the Swift for TensorFlow GitHub repository or build from source.
2. Install TensorFlow: S4TF requires TensorFlow. Follow the instructions on the TensorFlow website to install TensorFlow for Swift.
3. Set Up IDE: Choose an IDE that supports Swift development. Xcode is a popular choice for macOS users, and there are options like VS Code with Swift support for other platforms.

Building a Game-Playing AI

Choose a Game

Select a game to build your AI for. Simple board games like Tic-Tac-Toe or Connect Four are good starting points due to their manageable complexity.

Designing the AI

Here’s a basic approach to designing a game-playing AI in Swift for TensorFlow:

1. Game Representation: Define how the game state is represented. This could involve creating a data structure or class to hold the board state and current player turn.
2. Game Logic: Implement the rules of the game, including valid moves and checking win conditions.
3. AI Model: Define a neural network or another model that will learn to play the game. S4TF allows you to define models using its API, leveraging TensorFlow’s operations.
4. Training the Model: Use reinforcement learning or supervised learning techniques to train your AI model. This involves feeding game states (input) and desired outcomes (output) into the model.
5. Evaluation: Evaluate your model’s performance against human players or other AI agents to refine its strategy.

By combining Swift’s clarity with TensorFlow’s power, Swift for TensorFlow opens up exciting possibilities for developing game-playing AI applications. Dive in, experiment, and enjoy building intelligent game agents with S4TF.

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

Module 1 – Set Up Your Environment Quiz Answers

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

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

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

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

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

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.