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Interesting application of genetic algorithms. The weird thing about GA though, and any optimiser, is that you need an "error function" that you will us to det

Interesting application of genetic algorithms. The weird thing about GA though, and any optimiser, is that you need an "error function" that you will us to determine when one solution is better than another. Presumably he designed an error function for "fun", and his GA system finds local minima within it. But this is what I mean by "weird thing": you have to come up with a quantification of what makes a game fun, and _this_ is ultimately what will determine what kinds of solutions you come up with. Regardless of how the optimiser works, the error function is what the programmer is designing, and therefore the programmer is designing the space in which solutions will exist, so I would argue that any specific games are attributed to the programmer (or whoever designed the error function), and any specific game just happens to be one choice within the available search space. Unless you want to manually try every iteration and rate it from 1 to 10, this implies that it's possible to come up with a "funness" model of human game playing. That in itself is sort of a interesting thing, falling somewhere between philosophy and psychology, rather than computer science.

eru 15y ago
Given enough money, you can outsource the error function to mechanical turk. Or to go more meta (and possibly cheaper): Make designing a good error function part of a meta GA-run, and let those functions be judged by comparing their judgements with human judgements.
- ced 15y ago
  Why not go full Turk? Have a Turker suggest a new rule/modification. It will be paid for only if accepted by another, who must play N recorded moves of the game with a friend before passing judgment. Iterate. Disclaimer: I've never used MT. Is this possible?
- rndmcnlly0 15y ago
  Given the effort the play a game and the inter-subject noise in a numerical judgement, I think a better use of Mechanical Turk would be to ask the player to do some blame assignment. That is, instead of rating a game as 4-stars, they could perhaps give a thumbs up/down to particular rules or sets of rules in abstracted representation of the game. This kind of feedback extracts several more bits of information from the player than a single rating (making better use of them). However, it breaks the applicability of an evolutionary algorithm which treats both artifacts and fitness evaluation as black boxes. If you use a search algorithm that was aware of how the game was built from components, I'm guessing that component-level feedback (being both more objective and more specific) could provide more informative pressure to drive the algorithm than the standard interactive genetic algorithm setup gets.
  - eru 15y ago
    Yes, I also thought of giving multi-dimensional feedback, instead of just a single number, when writing the comment.
Nvn 15y ago
In the comments the author of the article refers to a paper[1] that describes the system (Ludi) that they're using. They came up with 57 aesthetics criteria which they used on games played by humans and compared the scores on the criteria to the human judgements. Based on this they picked the 16 best criteria and generated games. The fitness function is a score based on the criteria after n amount of automated playthroughs. These generated games tested by humans and they found that the aesthetics criteria and human judgements correlated significantly. Besides these criteria (they do not describe all criteria in depth but I assume some might be leaning towards being subjective in regards to the programmers preferences) they also add a restriction to how long the planning of a move for a game can take (15 seconds) and discarding the game if it surpasses this. Which as you say can be seen as designing the search space, but the results indicate that they've come up with a way to effectively measure the "fun" factor of the games they came up with. [1] http://www.cameronius.com/cv/publications/ciaig-browne-maire-19.pdf http://www.cameronius.com/cv/publications/ciaig-browne-maire...
- radarsat1 15y ago
  That is awesome. Thanks for pointing it out. Science!
esrauch 15y ago
The article didn't really go into detail about the criteria, but there are some easily quantifiable things that are desirable in a game if it is intended to be played 'seriously' (like Backgammon or Go, unlike the games "We didn't playtest this at all" or Munchkin which are just trying to be fun instead of legitimately competitive. A minimal first player advantage, turnabout (ability for someone to come from behind), balance between skill and randomness in games with luck. I think you could come up with metrics for interactivity, eg how much of it is 'goldfishing' vs choices that are notably different based on what the other player has done or will do. Dominion is an example where I think you could show that (for many configurations) an AI that ignores opponents actions would still win some significant portion of the time compared to an AI that takes it into account.
takeoutweight 15y ago
The problem of assigning a "fun value" to a game is an active area of research. Julian Togelius and Georgios Yannakakis are some of the big names looking at this topic right now. One approach is to use tried-and-true statistical techniques to build models from user feedback. Often a player will be provided two variations of a game and asked which one was more fun. This input, combined with various other factors (how did the player perform? how many enemies were there in each version?) help construct some sort of classifier to automatically evaluate new game variations. An alternate approach, which I explored during my Master's research on the topic, was to analyze existing levels in commercially released videogames to construct a model of fun from that, as opposed to considering subjective human feedback. I chose to analyse Super Mario Brothers. It turns out you can see a very characteristic pattern in how the difficult portions of the levels are arranged. This pattern of difficulty takes the form of a rhythmic interplay of difficult and easier portions, and can be nicely described in terms of Flow and the Yerkes-Dodson law. We used this a criterion for evaluating automatically generated levels as a part of a larger system. The applications of this are pretty wide. Perhaps we could automatically evaluate web page interactions according to a certain model of fun to engage users? Perhaps we can detect when people are not having fun and automatically generate alternate designs?
- rndmcnlly0 15y ago
  Here's my restatement of the statistical approach (correct me if I'm wrong): Instead of proposing some metric for fun that is valid a-priori, we are going to look for the very grounded game-specific correlates of fun and optimize with respect to those. Satisfactorily capturing the entire fuzzy sense of human fun in a finite piece of code is silly, but cataloging the observed feedback for every popular combination of level elements (e.g. "goomba two blocks to the right of a coin block while the player has a fire flower") is both practical and useful for automating level design.
jacques_chester 15y ago
A project I briefly explored was to develop an algorithmic model of "agency" in a roleplaying game, with the idea of taking existing plotline generation approaches and using the agency algorithm as a heuristic path-picker through the possible plotline alternatives.
rndmcnlly0 15y ago
It's natural to question how someone can unproblematically pose a metric on the value of game rulesets (particularly without a generous helping of philosophy and psychology to back it up). However, pragmatically, it's not a silly thing to try. If there were game-design IDEs in the future, we'd like them to provide the equivalent of spell/grammar check -- if there was an alternate design with an edit distance of two away from the current design that scored dramatically better on some canned metric, it might be worth putting a human eye on that alternative design. If you think of the error function as the combined output of a bunch of "obvious flaw" detectors instead of a "theory of fun", then speculative optimization where the initial conditions come from a human's design under consideration becomes a potentially interesting bit of design automation. Think of design rule checks in CAD with a bit of fuzziness and a default bias -- instead of saying just yes/no, it can say "X might be a better alternative according to the metrics you've enabled in the preference window, consider adopting some of its edits".

eru 15y ago

Given enough money, you can outsource the error function to mechanical turk. Or to go more meta (and possibly cheaper): Make designing a good error function part of a meta GA-run, and let those functions be judged by comparing their judgements with human judgements.

ced 15y ago
Why not go full Turk? Have a Turker suggest a new rule/modification. It will be paid for only if accepted by another, who must play N recorded moves of the game with a friend before passing judgment. Iterate. Disclaimer: I've never used MT. Is this possible?
rndmcnlly0 15y ago
Given the effort the play a game and the inter-subject noise in a numerical judgement, I think a better use of Mechanical Turk would be to ask the player to do some blame assignment. That is, instead of rating a game as 4-stars, they could perhaps give a thumbs up/down to particular rules or sets of rules in abstracted representation of the game. This kind of feedback extracts several more bits of information from the player than a single rating (making better use of them). However, it breaks the applicability of an evolutionary algorithm which treats both artifacts and fitness evaluation as black boxes. If you use a search algorithm that was aware of how the game was built from components, I'm guessing that component-level feedback (being both more objective and more specific) could provide more informative pressure to drive the algorithm than the standard interactive genetic algorithm setup gets.
- eru 15y ago
  Yes, I also thought of giving multi-dimensional feedback, instead of just a single number, when writing the comment.

Nvn 15y ago

In the comments the author of the article refers to a paper[1] that describes the system (Ludi) that they're using. They came up with 57 aesthetics criteria which they used on games played by humans and compared the scores on the criteria to the human judgements. Based on this they picked the 16 best criteria and generated games. The fitness function is a score based on the criteria after n amount of automated playthroughs. These generated games tested by humans and they found that the aesthetics criteria and human judgements correlated significantly. Besides these criteria (they do not describe all criteria in depth but I assume some might be leaning towards being subjective in regards to the programmers preferences) they also add a restriction to how long the planning of a move for a game can take (15 seconds) and discarding the game if it surpasses this. Which as you say can be seen as designing the search space, but the results indicate that they've come up with a way to effectively measure the "fun" factor of the games they came up with. [1] http://www.cameronius.com/cv/publications/ciaig-browne-maire-19.pdf http://www.cameronius.com/cv/publications/ciaig-browne-maire...

radarsat1 15y ago
That is awesome. Thanks for pointing it out. Science!

esrauch 15y ago

The article didn't really go into detail about the criteria, but there are some easily quantifiable things that are desirable in a game if it is intended to be played 'seriously' (like Backgammon or Go, unlike the games "We didn't playtest this at all" or Munchkin which are just trying to be fun instead of legitimately competitive. A minimal first player advantage, turnabout (ability for someone to come from behind), balance between skill and randomness in games with luck. I think you could come up with metrics for interactivity, eg how much of it is 'goldfishing' vs choices that are notably different based on what the other player has done or will do. Dominion is an example where I think you could show that (for many configurations) an AI that ignores opponents actions would still win some significant portion of the time compared to an AI that takes it into account.

takeoutweight 15y ago

The problem of assigning a "fun value" to a game is an active area of research. Julian Togelius and Georgios Yannakakis are some of the big names looking at this topic right now. One approach is to use tried-and-true statistical techniques to build models from user feedback. Often a player will be provided two variations of a game and asked which one was more fun. This input, combined with various other factors (how did the player perform? how many enemies were there in each version?) help construct some sort of classifier to automatically evaluate new game variations. An alternate approach, which I explored during my Master's research on the topic, was to analyze existing levels in commercially released videogames to construct a model of fun from that, as opposed to considering subjective human feedback. I chose to analyse Super Mario Brothers. It turns out you can see a very characteristic pattern in how the difficult portions of the levels are arranged. This pattern of difficulty takes the form of a rhythmic interplay of difficult and easier portions, and can be nicely described in terms of Flow and the Yerkes-Dodson law. We used this a criterion for evaluating automatically generated levels as a part of a larger system. The applications of this are pretty wide. Perhaps we could automatically evaluate web page interactions according to a certain model of fun to engage users? Perhaps we can detect when people are not having fun and automatically generate alternate designs?

rndmcnlly0 15y ago
Here's my restatement of the statistical approach (correct me if I'm wrong): Instead of proposing some metric for fun that is valid a-priori, we are going to look for the very grounded game-specific correlates of fun and optimize with respect to those. Satisfactorily capturing the entire fuzzy sense of human fun in a finite piece of code is silly, but cataloging the observed feedback for every popular combination of level elements (e.g. "goomba two blocks to the right of a coin block while the player has a fire flower") is both practical and useful for automating level design.

jacques_chester 15y ago

A project I briefly explored was to develop an algorithmic model of "agency" in a roleplaying game, with the idea of taking existing plotline generation approaches and using the agency algorithm as a heuristic path-picker through the possible plotline alternatives.

rndmcnlly0 15y ago

It's natural to question how someone can unproblematically pose a metric on the value of game rulesets (particularly without a generous helping of philosophy and psychology to back it up). However, pragmatically, it's not a silly thing to try. If there were game-design IDEs in the future, we'd like them to provide the equivalent of spell/grammar check -- if there was an alternate design with an edit distance of two away from the current design that scored dramatically better on some canned metric, it might be worth putting a human eye on that alternative design. If you think of the error function as the combined output of a bunch of "obvious flaw" detectors instead of a "theory of fun", then speculative optimization where the initial conditions come from a human's design under consideration becomes a potentially interesting bit of design automation. Think of design rule checks in CAD with a bit of fuzziness and a default bias -- instead of saying just yes/no, it can say "X might be a better alternative according to the metrics you've enabled in the preference window, consider adopting some of its edits".