Artificial Intelligence in Game Design

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Questions and Answers

What makes game playing a suitable problem for AI researchers?

It presents a good reasoning problem that is both formal and nontrivial.

List two characteristics that define games of strategy in the context of AI.

The games involve a sequence of moves to play and have rules that specify possible moves.

Describe the roles of the computer and the opponent in a two-player game tree.

In the game tree, the computer is designated as 'Max' and aims to maximize its utility, while the opponent is 'Min' and seeks to minimize the computer's utility.

What does the evaluation function do in evaluating a board position?

The evaluation function assesses how good a board position is by assigning a value based on static features. Signup and view all the answers

What do the values produced by the evaluation function signify?

A positive value indicates a winning position, a value of zero signifies a tie, and a negative value implies a losing position. Signup and view all the answers

How is the concept of a game tree utilized in AI for game playing?

A game tree models the possible moves and outcomes in a two-player game, allowing the AI to strategize by evaluating potential future positions. Signup and view all the answers

Why are evaluation functions critical in evaluating board positions in games?

Evaluation functions provide a quantitative measure of how favorable a board position is for a player, facilitating decision-making in strategy games. Signup and view all the answers

What does the 'zero-sum assumption' imply in the context of AI and game evaluation?

The zero-sum assumption indicates that one player's gain is equivalent to the other's loss, allowing a single evaluation function to adequately represent both players' interests. Signup and view all the answers

How do AI strategies in game playing compare to human strategies?

AI strategies can systematically evaluate a larger number of potential moves through computational power, often leading to more optimal decisions than humans can make. Signup and view all the answers

In a game tree, what roles does the 'Max' player and 'Min' player represent?

The 'Max' player seeks to maximize their score, while the 'Min' player aims to minimize the score of the 'Max' player during their turns. Signup and view all the answers

Study Notes

Artificial Intelligence in Games

AI in games is about implemented game design
Game AI is used to create the appearance of intelligence in non-player characters (NPCs)

Game Playing

Game playing is a good reasoning problem
Comparing game AI to human and other computer programs is relatively easy
Strategic games have a sequence of moves, rules for moves and payment per move and the goal of maximizing payoff

Kinds of Games

Strategy games are particularly important for AI study Characteristics of strategy games:
- Sequence of moves to play
- Rules defining possible moves
- Rules defining payment for each move
- Goal to maximize your payment

Game Tree

Computer is 'Max', opponent is 'Min'
The game tree shows possible game states
Terminal nodes are evaluated based on the utility function, a means to measure the 'goodness' or 'badness' of a position

Evaluation Functions

A board position's value is based on its static features, such as piece count and position
Zero-sum assumption: one function describes the position's goodness for both players – if one player is winning the other loses, vice versa
f(n) > 0 : Player is winning in position 'n'
f(n) = 0 : Position 'n' is tied
f(n) < 0: Opponent is winning in position 'n'
Evaluation functions can be developed using expert knowledge (e.g. the number of three-in-a-rows in Tic-tac-toe)

Example Chess Score

Black has 1 bishop and 2 rooks, score = 18
White has 5 pawns and 1 rook, score = 10
Overall score for the board position: black = 8, white = -8

Entire Search Space

Shows all possible positions of a game until a final result.

Partial Game Tree for Tic-Tac-Toe

MAX is X
MIN is O
The utility function determines the values at the leaf nodes (results of the game)
Backtracking values gives value to the tree nodes.

Mini-Max Terminology

Utility function: Evaluates the end position
Backed-up value: Value given to each node based on successors of the node.
Minimax procedure: A search algorithm to determine the optimal move. It searches several levels to find the highest achievable payoff.

Minimax

A perfect play strategy for deterministic games where players know the outcomes.
The goal is to find a move that results in the highest possible minimax value.
2-ply Games (2 player game) the example given shows a simplified tree, representing possible game outcomes.

Example

Possible moves given in a game board position
Evaluates each move (immediate results) generated by some static evaluator.

Example with Opponent's Moves

Shows calculation of the next move against opponent's moves generated by a static evaluator and its consequent positions of the game.
Showing how moves and the possible outcomes (static evaluations) are recursively combined.

This Process Continued

The minimax process can calculate all possible moves up to any level, as resources allow in a given amount of time.

Modified Game

Analyzing best decision at node level from the leaves upwards
Giving each node a value

Strategies Used

Minimax algorithm is used to find winning positions by exploring the possible outcomes of the game to the end.
Giving best strategy is for the 'MAX' player.

Minimax Example

Shows a calculation example of the minimax evaluation to determine the best possible move. Static evaluation function runs on the leaves of the tree structure.

Sample Evaluations

Shows example scenarios of a basic Tic-Tac-Toe board and evaluating each board state.

Example Tree With Chance

Shows how to deal with chance events in game trees.

Game Agents (1 of 2)

AI focuses on game agents (e.g. NPC)
Agents have a sense-think-act lifecycle
Gather current world state; including barriers, opponents and objects
Game agents are restricted at vision and hearing range.

Game Agents (2 of 2)

Evaluation and decision making using if-then logic statements and/or an effective search strategy
Pre-coded expert knowledge/ if-then statements with randomness help create unpredictability.
Search Algorithm: Finding the best/optimal solution.

Game Agents - Thinking (1 of 3)

Expert knowledge: FSM (finite state machines), decision trees
Often adequate for many AI tasks
Simple rules
Easy to develop

Game Agents - Thinking (2 of 3)

Search: Look ahead, calculating possible moves
Determining moves in game board, pathfinding
Machine learning: Past actions are evaluated, used for future too slow
ML Techniques promising, but too slow to be employed

Game Agents - Thinking (3 of 3)

Making agents "stupid"
Simple implementation for many cases
Agent cheating: Give agents an unfair advantage; for instance, more attributes/knowledge
Remember, that's the goal, to create a challenge for the player

Pac-Man

Game developed by Namco and distributed by Midway
Released in 1980
Single player only
Simple maze game

Ghosts

Ghosts move with a specific strategy, not random per say
Pac-Man chasing
Moves can be random but are not necessarily random

Sim City

Game developed by Maxis
Published by Electronic Arts
Released in 1989
Series of Simulation games, AI implemented at the forefront of technology

A Living World

Players can manipulate certain aspects of the city
City population acts somewhat autonomously
The game simulates random disasters

AI Learning

Training large creatures using reward/punishment mechanisms
Creatures learn to complete tasks based on reward or punishment, or based on the player's desired behaviour.

Game AI History - 1980

First computer games (1960s): Space War, chess against the computer
Atari (1970s): Pong, space invader
More complex games (1980s): Fighting games, Pac-man

Game AI History - 1990s

Madden Football, FPS (first-person shooter) and RTS(real time strategy): complex AI
RTS games, path finding issues due to complexity, enemy attacks in bee line fashion.

Game AI History - 2000s

More games utilized neural networks
Black and White (2001)
Collin McRae Rally 2 (2001)
Hyperthreading
More sophisticated AI engines, creates more realistic 3D environments (Core Duo)

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Description

This quiz explores the role of Artificial Intelligence in games, particularly focusing on its implementation in game design and the behavior of non-player characters (NPCs). It covers various types of games, including strategy games, and the use of game trees and evaluation functions to analyze game moves. Test your knowledge on how AI improves gameplay and strategic decision-making.