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# Lesson 8: Track A Summaries

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Schedule   ::   Lesson 8   ::   Track A Summaries   ::   Track B Summaries

#### Crawford, C., Chapter 6: design techniques and ideals

In this chapter Crawford explains important design techniques to increase the player’s motivation and desire to play the game. He discusses AI, or what he terms “artificial smarts,” and explains the importance of a game producing reasonable behavior and unpredictability at the same time. He also explains way to balance games. Triangularity is also discussed as a way to make games more interesting and yet keep them balanced. Triangulation is a basic form of indirection, introducing a third party to the game. Crawford gives several examples of games displaying various levels of indirection. Learning curves are explained as “a series of related games (p. 12)” which, ideally, should transition smoothly during game play. The author ends by discussing winnability, describing it as an illusion because it must appear that the game is winnable at all times to all player levels, yet the game must avoid actually being winnable or its appeal will quickly be lost.

Requirements for an “artificial smarts system”:

• Should produce reasonable behavior, patterns that should be somewhat predictable (p. 2)
• I.E. no shocks, computer should not allow it’s characters to pass through walls, fly, etc. unless that is part of the game
• Should be unpredictable (p. 2)
• While still being reasonable, computer should be hard to anticipate
• Computer should focus on a single aspect of a larger pattern
• Better algorithms look at decisions in the largest context possible, which allows them to take in a lot of information in a single rule
• A point system
• Give each kind of move a certain amount of points, which no two moves having the same value. The move with the higher point value is the one executed
• It is hard to accurately assign points to moves, it requires a lot of experimentation
• Field analysis (p. 3)
• The computer can calculate a large variety of factors of nearby opponents, obstacles, etc. and then make the analysis as to the best course of action
• Change the game (p. 4)
• If you can’t find some way to make a certain feature work with the AI, then just take it out. It will save trouble in the end
• Hard to coordinate moves of many units at once
• Use an array of “virtual moves” where the computer can plan its moves until a successful pattern is found. Then the moves are actually carried out
• To make games more interesting, there should be multiple algorithms, with the computer transitioning between them. The transitions should be subtle, however
• The above works great for special systems, but for not special, use differential equations
• Use a damping factor to make values more interesting (p. 5)
• Even with all the algorithms available, the computer must still be at least twice as powerful to stand up to a human opponent

Limited Information

Limit the amount of information the human player has access to. Because humans are superior processors of information, this can handicap them effectively.

• Don’t just randomly limit or this is frustrating.
• If limiting is done well, it can engender the curiosity of the player

Pace of the game

You can also speed up the pace, i.e. the computer can calculate faster than the human. This deprives the player of the time they need to figure things out. Don’t overuse this technique, though. (p. 6)

Summary of above 4 balancing techniques

Use a small amount of each type if possible.

Relationships between opponents

• Symmetric relationships
• Each player has the same strengths, attributes, weaknesses, etc.
• This does provide for great balance
• This can make the game overly simple. Any good strategy will be used by both sides. Subtle advantages can provide great rewards (i.e. like having an extra pawn in chess)
• Asymmetric relationships
• Each player has a distinct combination of many attributes, advantages, and disadvantages (p. 6)
• These combinations must be carefully balanced so that both sides have an equal chance of victory
• Can be done with “plastic symmetry”, where the games start symmetric, but players can choose different combinations that change up the situation. The player chooses whether the situation is balanced or not (p. 7)
• Game can start asymmetric
• Indirection (Triangularity) (p. 7)
• Non-transitive games, i.e. rock paper scissor. Just because one combination holds to win, it does not mean that it will hold for other combinations
• Allows indirect methods of approaching and attacking, i.e. a player can use a mix of offensive and defensive strategies in a conflict with an opponent
• Actors and indirect Relationships (p. 8)
• An actor is not a direct participant or opponent. It is a third-party indirect agent that the player can work through to attack the opponent
• Indirection makes for more interesting games, especially when no none side is directly associated with the players

Smooth Learning Curves

This refers to a player’s score as a “function of time spent with the game”, i.e. the score should go higher the longer the player plays the game (p. 9)

The ideal curve should always slope upwards smoothly. If the curve ever drops mid-game, it means the game is contradictory (p. 9)

In order to keep this smooth curve, the computer ideally should adapt to the player’s skill level by increasing difficulty. Another method is to allow the player to choose their difficulty level (p. 9)

The Illusion of Winnability

The game should appear to be winnable, but not be truly beatable or winnable, because then players will lose their motivation to play. Pac-Man is a good example because you appear to win (by eating all of the dots), but you never actually do because the game just keeps getting more difficult (p. 10)

A good game encourages players to experiment; it should not give away the fact that you can’t ultimately win it.

If a player keeps dying in the game, or losing, he or she should attribute it to their own errors, which are correctable. If this is the case, the player will want to play again, correcting their past mistakes. This losing should not be attributed to bad game controls, or an impossibly hard game. (p. 10)

“Another important trait of any game is the illusion of winnability. If a game is to provide a continuing challenge to the player, it must also provide a continuing motivation to play. (p. 12)”

“A smooth learning curve is worked into a game by providing a smooth progression from the beginner’s level to an expert level. This requires that the game designer create not one game but a series of related games. Each game must be intrinsically interesting and challenging to the level of player for which it is targeted. Ideally, the progression is automatic; the player starts at the beginner’s level and the advanced features are brought in as the computer recognizes proficient play. (p. 12)”

In summary, each game designer must use all of the above techniques and skills masterfully to come up with their own individual technique. This is much like artists who develops their own personal style of technique and form.

#### Related articles/class discussions:

• Article name (Lesson 7 Track A): This article relates to Crawford’s last chapter on game design sequence. In this chapter he expands on some of the essential elements necessary to motivate players and provide a continuous challenge as the game progresses.
• Chapter 8: The Internal Economy of Games and Game Balancing (Lesson 7 Both Tracks): Intransitive relationships are like the triangular relationships mentioned above. This article also talks about symmetry and balancing between opponents.
• Class discussion: This relates to the development of the SRA game. The focus should be more on design less on the code to end up with a worthwhile game.

#### Discussion points/questions:

• Why is so important for the player to perceive that the game is winnable at all times?
• What are some other ways in which to give the illusion of winnability?
• What are some techniques for the computer to adapt difficulty level as the player increases in skill?
Contributors: Tom Caswell, Marion Jensen, Jennifer Jorgensen, Jon Scoresby, and Tim Stowell
Copyright 2008, by the Contributing Authors. Cite/attribute Resource . admin. (2008, May 20). Lesson 8: Track A Summaries. Retrieved January 07, 2011, from Free Online Course Materials — USU OpenCourseWare Web site: http://ocw.usu.edu/instructional-technology-learning-sciences/instructional-games/Lesson_8__Track_A_Summaries.html. This work is licensed under a Creative Commons License
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