Abstract: "We define data games as games where gameplay and/or
game content is based on real-world data external to the
game, and where gameplay supports the exploration of and
learning from this data. This concept is discussed in rela-
tion to open data, procedural content generation and serious
games, and research challenges are outlined. To illustrate
the concept, we present six prototype games and content
generators of our own making. We also present a tentative
taxonomy of actual and potential data games, and situate
the described games within this taxonomy."
"A data game is a game that allows the
player(s) to explore data that is derived from outside the
game, by transforming the data into something that can be
played with. In other words, games as a form of interactive
data visualisation."
From the abstract: "Collective Artificial Intelligence (CAI) simulates human intelligence from data contributed by many
humans, mined for inter-related patterns. This thesis applies CAI to social role-playing, introducing an
end-to-end process for compositing recorded performances from thousands of humans, and simulating
open-ended interaction from this data. The CAI process combines crowdsourcing, pattern discovery, and
case-based planning. Content creation is crowdsourced by recording role-players online. Browser-based
tools allow non-experts to annotate data, organizing content into a hierarchical narrative structure.
Patterns discovered from data power a novel system combining plan recognition with case-based
planning. The combination of this process and structure produces a new medium, which exploits a
massive corpus to realize characters who interact and converse with humans. This medium enables new
experiences in videogames, and new classes of training simulations, therapeutic applications, and social
robots. .... As a proof of concept, a CAI system has been evaluated by recording over 10,000 performances
in The Restaurant Game, automating an AI-controlled waitress who interacts in the world, and
converses with a human via text or speech. Quantitative results demonstrate CAI supports significantly
open-ended interaction with humans, while focus groups reveal factors for improving engagement."
Abstract: "A number of changes are occurring in the field
of computer game development: persistent online games, digital
distribution platforms, social and mobile games, and the emer-
gence of new business models have pushed game development to
put heavier emphasis on the live operation of games. Artificial
intelligence has long been an important part of game development
practices. The forces of change in the industry present an
opportunity for Game AI to have new and profound impact on
game development practices. Specifically, Game AI agents should
act as "producers" responsible for managing a long-running set
of live games, their player communities, and real-world context.
We characterize a confluence of four major forces at play in the
games industry today, together producing a wealth of data that
opens unique research opportunities and challenges for Game
AI as producers. We enumerate 12 new research areas spawned
by these forces and steps toward how they can be addressed by
data-driven Game AI Producers"
Abstract: "The rich interaction space of man
y educational games presents a
challenge for designers and researchers who
strive to help
players
achieve specific learning outcomes. Giving players a large amount
of freedom over how they perform a complex game task makes it
difficult to
anticipate what t
hey will do.
In order to ad
dress this
issue
designers must ask
:
what are students
do
ing in my game?
And
does
it
embody
what I intended them to learn?
To answer
these questions, designers need methods to expose the details of
student play.
We describe our a
pproach for
automatic
extract
ion
of
conceptu
al features
from logs of student play sessions within an
open educational game
utilizing a
two
-
dimensional context
-
free
grammar. We demonstrate how these features can be used
to clu
s-
ter student solutions in the e
ducational game
RumbleBlocks
. U
s-
ing these clusters
,
we explore the range of solutions and measure
how many students
use the designers'
envisioned
solution
.
Equipped with this information, designers and researchers can
focus redesign efforts to areas in the
game where discrepancies
exist between
the
designer
s'
intention
s
and player experience
s."