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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

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

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

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

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!

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

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 with Swift for TensorFlow (S4TF) is an exciting area where you can combine the power of Swift programming language with the flexibility and efficiency of TensorFlow to create intelligent agents capable of playing and mastering various games. Here’s an introduction to get you started:

  1. Understanding Swift for TensorFlow (S4TF):
    • Swift for TensorFlow is an open-source machine learning library developed by Google and the Swift community.
    • It seamlessly integrates Swift programming language with TensorFlow, allowing you to leverage Swift’s expressive syntax and powerful features for building machine learning models.
  2. Game-playing AI:
    • Game-playing AI involves creating intelligent agents that can play and excel at various games.
    • These agents typically utilize techniques from reinforcement learning, a branch of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards.
  3. Getting Started:
    • Install Swift for TensorFlow: Begin by installing Swift for TensorFlow on your system. You can find installation instructions on the official Swift for TensorFlow GitHub repository.
    • Familiarize Yourself with Swift: If you’re new to Swift, it’s essential to understand the basics of the language, including variables, control flow, functions, and object-oriented programming concepts.
    • Learn TensorFlow Basics: While Swift for TensorFlow abstracts many complexities, having a basic understanding of TensorFlow concepts such as tensors, operations, and graphs will be beneficial.
    • Choose a Game: Select a game you want your AI agent to play. Popular choices include classic board games like chess, Go, or video games like Atari games.
    • Define the Environment: Set up the game environment in a way that your AI agent can interact with it. This may involve creating a simulator or using existing game environments provided by libraries like Gym or Swift for TensorFlow’s own SwiftAI.
    • Implement the AI Agent: Develop an AI agent using reinforcement learning techniques such as Q-learning, Deep Q-Networks (DQN), or policy gradient methods like REINFORCE. Implement the agent’s actions, rewards, and learning algorithms in Swift.
    • Train and Evaluate: Train your AI agent using the game environment. Continuously evaluate its performance and tweak the learning parameters to improve its gameplay.
  4. Advanced Techniques:
    • As you become more comfortable with game-playing AI, you can explore more advanced techniques such as deep reinforcement learning, which involves using deep neural networks to approximate the Q-values or policy function.
    • You can also experiment with other algorithms such as Monte Carlo Tree Search (MCTS) for games that have a high branching factor like Go.
  5. Resources:
    • Swift for TensorFlow documentation and tutorials provide a wealth of information to help you get started.
    • Books and online courses on reinforcement learning and game-playing AI can deepen your understanding of the concepts and techniques involved.
    • GitHub repositories and online communities often share code and insights into implementing game-playing AI with Swift for TensorFlow.

By diving into game-playing AI with Swift for TensorFlow, you’ll not only learn about machine learning techniques but also hone your Swift programming skills while creating intelligent agents capable of mastering various games.

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