Tag Archives: game design

Goals, Anti-Goals and Multi-player Games

In this article I will try to address Keith Burgun‘s assertion that games should have a single goal and his analysis of certain kinds of goals as trivial or pathological. I will try to demonstrate that multi-player games either reduce to single player games or necessitate multiple goals, some of which are necessarily the sorts of goals which Burgun dismisses as trivial. I’ll try to make the case that such goals are useful ideas for game designers as well as being necessary components of non-trivial multi-player games.

(Note: I find Keith Burgun’s game design work very useful. If you are interested in game design and have the money, I suggest subscribing to his Patreon.)

Notes on Burgun’s Analytical Frame

The Forms

Keith Burgun is a game design philosopher focused on strategy games, which he calls simply games. He divides the world of interactive systems into four useful forms:

  1. toys – an interactive system without goals. Discovery is the primary value of toys.
  2. puzzle – bare interactive system plus a goal. Solving is the primary value of the puzzle.
  3. contests – a toy plus a goal all meant to measure performance.
  4. games – a toy, plus a goal, plus obfuscation of game state. The primary value is in synthesizing decision making heuristics to account for the obfuscation of the game state.

A good, brief, video introduction to the forms is available here. Burgun believes a good way to construct a game is to identify a core mechanism, which is a combination of a core action, a core purpose, and a goal. The action and purpose point together towards the goal. The goal, in turn, gives meaning to the actions the player can take and the states of the interactive system.

On Goals

More should be said on goals, which appear in many of the above definitions. Burgun has a podcast which serves as a good long form explication of many of his ideas. There is an entire episode on goals here. The discussion of goals begins around the fifteen minute mark.

Here Burgun provides a related definition of games: contests of decision making. Goals are prominent in this discussion: the goal gives meaning to actions in the game state.

Burgun raises a critique of games which feature notions of second place. He groups such goals into a category of non-binary goals and gives us an example to clarify the discussion: goals of the form “get the highest score.”

His analysis of the poorness of this goal is that it seems to imply a few strange things:

  1. The player always gets the highest score they are capable of because the universe is deterministic.
  2. These goals imply that the game becomes vague after the previous high score is beaten, since the goal is met and yet the game continues.

The first applies to any interactive system at all, so isn’t a very powerful argument, as I understand it. Take a game with the rules of Tetris except that the board is initialized with a set of blocks already on the board. The player receives a deterministic sequence of blocks and must clear the already present blocks, at which point the game ends. This goal is not of the form “find the highest score” or “survive the longest” but the game’s outcome is already determined by the state of the universe at the beginning of the game. From this analysis we can conclude that if (1) constitutes a downside to the construction of a goal, it doesn’t apply uniquely to “high score” style goals.

(2) is more subtle. While it is true that in the form suggested, these rules leave the player without guidelines after the goal is met, I believe that in many cases a simple rephrasing of the goal in question resolves this problem. Take the goal:

G: Given the rules of Tetris, play for the highest score.

Since Tetris rewards you for clearing more lines at once and since Tetris ends when a block becomes fixed to the board but touches the top of the screen, we can rephrase this goal as:

G': Do not let the blocks reach the top of the screen.

This goal is augmented by secondary goals which enhance play: certain ways of moving away from the negative goal G' are more rewarding than others. Call this secondary goal g: clear lines in the largest groups possible. Call G' and goals like it “anti-goals.”

This terminology implies the definition.

If a goal is a particular game state towards which the player tries to move, an anti-goal is a particular state which the player is trying to avoid. Usually anti-goals are of the form “Do not allow X to occur” Where X is related to a (potentially open ended) goal.

Goals of the “high score” or “survive” variety are (or may be) anti-goals in disguise. Rephrased properly, they can be conceived of in anti-goal language. Of course there are good anti-goals and bad ones, just as there are good goals and bad goals. However, I would argue that the same criteria applies to both types of goals: a good (anti) goal is just one which gives meaning to the actions a person is presented with over an interactive system.

Multi-Player Games and Anti-Goals

I believe anti-goals can be useful game design, even in the single player case. In another essay I may try to make the argument that anti-goals must be augmented with mechanics which tend to move the player towards the anti-goal against which players must do all the sorts of complex decision making which produces value for players.

However, there is a more direct way of demonstrating that anti-goals are unavoidable aspects of games, at least when games are multi-player. This argument also demonstrates that games with multiple goals are in a sense inevitable, at least in the case of multi-player games. First let me describe what I conceive of as a multi-player game.

multi-player game: A game where players interact via an interactive system in order to reach a goal which can only be attained by a single player.

The critical distinction I want to make is that a multi-player game is not just two or more people engaged in separate contests of decision making. If there are not actions mediating the interaction of players via the game state then what is really going on is many players are playing many distinct games. A true multi-player game must allow players to interact (via actions).

In a multi-player game, players are working towards a win state we can call G. However, in the context of the mechanics which allow interaction they are also playing against a (set of) anti-goals {A}, one for each player besides themselves. These goals are of the form “Prevent player X from reaching goal G“. Hence, anti-goals are critical ingredients to successful multi-player game design and are therefore useful ideas for game designers. Therefore, for a game to really be multi-player then there must be actions associated with each anti-goal {A}.

An argument we might make at this point is that if players are playing for {A} and not explicitly for G then our game is not well designed (for instance, it isn’t elegant or minimal). But I believe any multi-player game where a player can pursue G and not concern herself with {A}, even in the presence of game actions which allow interaction, is a set of single player games in disguise. If we follow our urge to make G the true goal for all players at the expense of {A} then we may as well remove the actions which intermediate between players and then we may as well be designing a single player game whose goal is G.

So, if we admit that multi-player games are worth designing, then we also admit that at least a family of anti-goals are worth considering. Note that we must explicitly design the actions which allow the pursuit of {A} in order to design the game. Ideally these will be related and work in accord with the actions which facilitate G but they cannot be identical to those mechanics without our game collapsing to the single player case. We must consider {A} actions as a separate (though ideally related) design space.

Summary

I’ve tried to demonstrate that in multi-player games especially, anti-goals, which are goals of the for “Avoid some game state”, are necessary, distinct goal forms worth considering by game designers. The argument depends on demonstrating that a multi-player game must contain such anti-goals or collapse to a single player game played by multiple people but otherwise disconnected.

In a broader context, the idea here is to get a foot in the door for anti-goals as rules which can still do the work of a goal, which is to give meaning to choices and actions in an interactive system. An open question is whether such anti-goals are useful for single player games, whether they are useful but only in conjunction with game-terminating goals, or whether, though useful, we can always find a related normal goal which is superior from a design point of view. Hopefully, this essay provides a good jumping off point for those discussions.


Quick Thoughts about Interactive Fiction

I’ve recently started a podcast called Text Adventure Purgatory wherein myself and several friends play and talk about Text Adventures/Interactive Fiction. Doing so has crystalized, in my mind, a few thoughts have been in mere fluid suspension in the back of my head about games and fun in general.

“A Theory of Fun for Game Design,” by Raph Koster asserts the following basic premise: fun is learning. This predicts that if a game offers to you a system which you can learn, then you will have fun playing it up until you have exhausted either the system or your capacity to continue learning about it. It’s silly to suggest that this theory covers everything that is fun or everything we might want to assert is a game (this kind of idealism is counterproductive in any context, if you ask me), but it is, I would argue, a useful one.

What is learning, anyway? I think neuroscience and contemporary machine learning techniques (which are inspired by and inspire neuroscience) can provide us with a useful model of the process: learning is an optimization problem which attempts to map inputs onto “desired” outputs or outcomes. Eg: the pixels (and their history) on a screen are mapped by our brains into a series of button presses which result in Mario reaching the end of the screen, where he touches the flag pole. Better than just describing the process, we now have a reasonable idea of how it happens too, and how to imitate the process in software.

There are lots of techniques for the latter, but they basically boil down to optimizing an objective function (the mapping from input to output) by exploring the input space, finding, and following trends in the output space. That is, start with a naive model, take some characteristic input data, apply the model to it, measure the outcome, make small changes to the model to improve the outcome (lots of strategies for this step), repeat until the model behaves well enough for your purposes. In the brain this happens by adjusting synaptic weights (and other physiological properties) of the neurons in question. In computerized learning systems this occurs by modifying the numerical parameters of the model.

Now we are ready for the point of this reflection: text adventures and interactive fiction provide too sparse a set of inputs and outputs to meaningfully train a system for playing. They (generally, I’m sure exceptions exist or attempt to exist) don’t provide a rich enough state space for learning, and hence they aren’t fun in the way that “A Theory of Fun” proposes we interpret that word.

What do I mean by “too sparse?” I mean, for one thing, that for any state in the game I can specify some non-perverse measure of similarity and value for that measure which has the following property: there will be no neighboring states included within that boundary. This is in contrast to games which involve simulated motion in space, which is, for the purposes of our discussion, continuous (that computers actually only simulate discrete spaces is not really material to the discussion: they are discrete spaces of sufficient granularity that our brains perceive them to be continuous).

For instance, there is a state in Deadline, the infocom game we played for several episodes in TAP, wherein the player character has discovered several pieces of broken china in the rose garden near the balcony of the library in which a murder has taken place. We arrive at this state only and exactly when a particular sequence of events (amounting, in isolation, to a few turns in the right order) has occured. There is nothing to refine about the process of reaching this state: either you perform the sequence of actions that produce this outcome or you do not so perform them.

A bit of reflection reveals how much in contrast this is with more typical videogames: in Super Mario Brothers, for instance, there are effectively an infinite number of ways to touch (to specify a single instance) the final flagpole in each level. As we vary the exact moment we press the jump botton, where we jump from, how long we hold it, how long we have run before, we refine the final state of interest and can find a solution which maximizes our height on the pole. There is a continuum of input states and output states (and a clear way of measuring our success) which allows those learning circuits (to use a drastically oversimplifying colloquialism) to grab onto something.

When playing a text adventure, in contrast, we essentially have nothing to do but explore, often by brute force, the state space the game gives us branch by branch until we find the final state. This is not usually fun, and using the context clues embedded in the text rarely helps: they can be either obtuse, in which case we are in the first strategy, or obvious, in which case there isn’t much to do but follow their instructions and traverse the graph. This problem is exacerbated by the fact that text adventures present themselves to us as text, creating the illusion of a rich, detailed world where, computationally, the exact opposite is true: everything reduces to a set of nodes connected by edges. Labeling more than one of those edges as “ending” the game helps a little: we can repeat the experience and land at different ending nodes by virtue of knowledge obtained on previous playthroughs, but we are still jumping for discrete state to discrete state, connected by discrete edges of low cardinality.

This isn’t a dig at interactive fiction: it is a way of explaining why it doesn’t “play like” other kinds of videogames, despite sharing a medium (computers). Novels, for instance, are even more restricted than interactive fiction: they proceed only and exactly in one way and come to life only and exactly as we read them.

Maybe these reflections tell us what we already know: that interactive fiction is more literature than game and that we should look elsewhere than traditional videogame experiences for an interpretive strategy which will allow us to discuss interactive fiction meaningfully.