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Forgot to mention this: Earl Grey won “Best Puzzles” in this year’s XYZZY awards! Rob and I were ecstatic with this result, especially considering the tough competition. IFWiki has a list of all the award winners and nominees, along with links to reviews and downloads. You should, of course, go play Earl Grey if you haven’t already, but there is so much more in that list deserving of your time—Blue Lacuna in particular lives up to every bit of the praise it has received.

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Another Castle

I’ve been in busyland lately, but I thought I’d post a quick update with a link to my appearance on Another Castle, NYC’s premier game design-related podcast. It was a really fun conversation! Charles and I discussed a number of things, including the Interactive Fiction scene, conversation (and language in general) in games, and games as texts. Here’s the audio file, but you really should go subscribe; the archives are amazing, and there’s a great new interview on a regular basis.

Just made a post this morning on Warp Skip regarding Foursquare, XBL achievements and usability. Here’s an excerpt:

Gamerscore and achievements serve a similar purpose. They give you feedback on your play; they give you acknowledgment when you do something noteworthy; they let you know (in broad terms) how much of a game’s content you’ve completed; they let you compare the way you’re playing the game to the way your friends are playing it. Achievements are one of the reasons I prefer playing games on the 360 to playing games on (for example) the Wii: more feedback, more context, makes for a more fun gaming experience.

With a few notable exceptions, no one plays games just for the achievements. They’re not a goal in and of themselves. Likewise, no one “plays” Foursquare just to get the badges. Both badges and achievements are there to let you know that your activities follow a particular pattern. As an added benefit, badges and achievements you haven’t earned yet suggest what other patterns are possible.

I wrote this before I read Sirlin’s response to Jesse Schell’s lecture, in which we are urged “to be vigilant against external rewards” (such as achievements). “How resistant are you to letting others manipulate you with hollow external rewards?” asks Sirlin. Obviously, I am much more sanguine about achievements—I think that people like them because they are useful and fun—and hope to argue for this more effectively in a future post.

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Lumarca

Friend and colleague Matt Parker just posted a video of Lumarca, an open-source volumetric 3D display. Lumarca was Matt’s thesis project at ITP, and appeared most recently at SIGGRAPH Asia:

It’s pretty awesome.

Gizmodo has a good roundup of the technologies behind a number of commercial 3D displays. Take a look and you’ll see that none of this technology really has a chance of getting into the hands of artists, makers and hackers any time soon. Lumarca, however, is ready to go. Here’s the build instructions and source code.

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Weekend roundup

A few quick links:

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On top, we have a table of truth values that result when comparing values of different types in PHP. On bottom, we have a chart illustrating the strengths and weaknesses of the seventeen types of Pokémon. Is it crazy to wonder whether one might have influenced the other?

The former is a matrix of arbitrary values intended to produce convenience. The latter is a matrix of arbitrary values intended to produce fun. In my experience, neither achieves its goal. But both follow an arbitrary logic, strangely twisted through history and culture, that might someday make a good subject for a Ph.D. thesis. (Why does a non-empty string equate with integer zero? Why is Psychic strong against Poison?)

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The Glasgow Science Center is currently exhibiting The Joking Computer, a kiosk-based installation running software made by artificial intelligence researchers at the University of Aberdeen. The software uses phonetic information about English words and semantic information from WordNet to generate pun-based riddles. (More information about how it works.)

A few of my favorites:

What kind of tree is nauseated?
A sick-amore

What do you call a cross between an emporium and a success?
A department score

What do you get when you cross a choice with a meal?
A pick-nic

More examples here, and an online version is promised soon.

I like to read programs like this—programs that generate text conforming to a specific genre—as a kind of ethnographic criticism. The Joking Computer describes a particular type of joke by observing how jokes of that type are formed and used in our culture, and then formalizing the jokes as a procedure. The procedure itself serves as a statement about how that genre of text works: its structure and its limitations.

The Joking Computer specifically, and text generators in general, also manifest the nature of the data that they’re built upon. Take this joke (please):

What do you call a washing machine with a september?
An autumn-atic washer.

This joke shows the gaps that exist in the program’s data, and the unique way in which the program uses that data. First, the program doesn’t have a way to know that a washing machine isn’t a kind of thing that is likely to be “with” a “September” (or that September isn’t a noun likely to be used with an indefinite article). Second, the relationship between “September” and “autumn” depends on the eccentricity of WordNet, which claims that there is a meronymic relationship between the two concepts. The joke is constructed on the basis of the fact that September is a “part of” autumn—which certainly makes a kind of sense, but isn’t necessarily something that most people would intuitively agree with. The joke, as a consequence of (at least) these two factors, seems stilted and alien.

Then again, stilted or not, I happen to think this joke (“autumn-atic washer”!) is hilarious, and that the humor stems at least in part from the gaps in the data and way the program uses that data. Jokes, after all, are funny when they provide surprising juxtapositions or reconceptualizations of things in the world, and the program delivers those in abundance.

Poems succeed when they make use of similar juxtapositions and reconceptualizations. I think that this is why generative text programs are most effective when they are designed to generate text in these genres (humor and poetry). These programs succeed just because they don’t perfectly model the genre they set out to emulate.

In other genres, like conversation or narrative, surprising juxtapositions are less valued, or even specifically forbidden. I think that generative algorithms in those genres tend to be less successful for this very reason. But that’s a subject for another post.

(The Joking Computer via, more info)

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Recent projects

Three recent projects which I have so far neglected to post about:

LATE EDIT: Andy Doro is exhibiting the Networked Byte Organ, which we worked on together, at Taller Boricua.

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Recently Leonard Richardson and I recorded a conversation about the popular DS game Scribblenauts. The result is called The Trouble with Scribbles. The conversation ranged from NetHack to Star Trek to Japanese mythology; I think somewhere along the way we managed to have a genuine insight or two. Listen in and tell us what you think.

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Facebook Twitter
Verbs
get
set
ban
unban
create
expire
promote
revoke
run
add
remove
register
cancel
edit
invite
rsvp
delete
refresh
query
is/are
upload
send
mark
publish

Nouns
allocation
property
metric
restriction
user
info
token
session
authorization
permission
batch
comment
friend
count
cookie
member
tag
url
handle
group
translation
link
string
message
thread
folder
note
notification
list
e-mail
page
fan
photo
album
profile
sms
stream
option
like
filter
status
video
limit

Verbs
get
show
update
destroy
post
put
exists
verify
end
follow
leave
report
request
authorize
authenticate

Nouns
search
trend
status
timeline
mention
retweet
friend
follower
direct message
friendship
id
account
session
delivery device
color
image
profile
favorite
notification
block
spam
search
token
test

I made this list by combing through both services’ API documentation and extracting noun-like words and verb-like words from the names of resources or methods. Facebook count: 24 verbs, 43 nouns. Twitter count: 15 verbs, 24 nouns.

Here’s what I think these numbers mean.

Facebook manages many different kinds of content, and allows you to perform many different kinds of actions on that content, though the actions that you can perform on one kind of content are inconsistent with the actions you can perform on another. Twitter has fewer types of content, and a more consistent set of HTTP-like actions to perform on that content.

The Facebook API tends toward the baroque and insular, while the Twitter API tries its best to be a part of the web. In general, the Twitter API is much more straightforward.

I think this simplicity—this paucity of nouns and verbs—has been an important factor in Twitter’s widespread growth among both users and developers.

Developers can be confident that—even if Twitter’s API changes—they’ll still be doing mostly the same actions (getting, posting, updating) on mostly the same things (statuses, friendships, direct messages). Users know exactly how all the moving parts of Twitter work together, and are therefore better able to understand how a given application might augment that.

On Facebook, the opposite is true. User statuses, notifications, event invitations, feed stories, photos—they all have different interfaces and behave in different ways. Even long-time developers can’t keep track of how everything works together.

Interestingly, Facebook’s platform roadmap says that one of Facebook’s goals is to “focus [...] communication on the stream and Inbox,” which will make communication on Facebook feel much more like Twitter. I think this is a smart move—anything that simplifies the user’s model of how communication on Facebook works is a good idea, both for users and developers.

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