Hi this is Tommy Thompson and welcome to AI and Games - a series about AI applications
and research in video games.
Today we're going to look at some really interesting research conducted in the world of Valve's
Team Fortress 2.
If you're not familiar, TF2 is a vastly popular online FPS with a fanbase and support that
stems back to almost a decade ago.
It's rare that a FPS continues to carry such a strong community.
However, it's fair to say that TF2 has continued to evolve as a platform, with significant
changes to this weird and eclectic shooter since its original launch back in 2007.
But it's not the game itself we're interested in today, given there's no real AI involved
in this objective-based shooter.
Rather, we're going to look at its community.
Community interactions are par-for-the-course these days in game development and modelling
the reactions and behaviour of the community even more so.
But what about how we identify or express ourselves within these games platforms?
Cosmetic modifiers are the norm in most modern online games: from the calling cards and emblems
of Call of Duty and Titanfall 2, to the costumes and colour schemes of games such as Halo 5
and Overwatch, there's plenty of ways in which to express yourself within the confines of
you chosen death-dealing avatar.
It's with this in mind we turn to Team Fortress 2 and look specifically at the usage of Hats:
the first phase in a suite of cosmetic items that have slowly had a stronger influence
on gameplay in more recent updates of the game.
What can we learn about people who wear hats?
Are there any correlations between those who wear hats and their prominence within the
TF2 community?
What about the type of hat that you're wearing; what does that say about you?
With a little bit of AI research, we can find the answers to all of these questions, so
let's go take a look.
Research into Team Fortress 2 hat-usage was conducted by MIT grad student Chong-U Lim
as part of a masters thesis.
The focus of the work was to see whether the range of hats available and the rarity of
certain hats, combined with the diversity of hats worn by players can tell us something
about the players themselves.
So you might think, geez, this is a lotta talk about hats, but purely cosmetic items
are an interesting phenomena in online games given they often act as an expression of achievement
that is not necessarily tied to a players skill.
The meaning or value of a hat ranges widely from person to person, given personal tastes
that clash or meld with the exaggerated and often absurdist art styles of game such as
TF2 or Overwatch.
This melding of the players persona with their in-game representation as denoted in the research
as 'phantasmal identities': a blend identity between the avatar in a video game with the
real-world perceptions and beliefs of the human player.
This actually leads to a form of projection of the players identity in an often ridiculous
and exaggerated fashion.
Lim gathered profile data from 200 players on Steam as part of the research process:
gathering information on how they interact in Steam forums, communicate on each others
profiles as well as gather a stack of information on item acquisition for Team Fortress 2.
With this data in-hand, Lim conducted a number of processing techniques and statistical methods
in order to garner useful data from it.
Now bear with me as we work our way through this as it gets a little complex.
Players are categorised by two distinct metrics that are used in greater detail throughout
the research later on: status performance and tie strength.
Status performance is a relationship between of player performance and avatar customisation.
This is largely reliant on establishing the value of items players own and the way in
which they perform their self-expression.
To do this, Lim gathered data from a third-party pricing site for hats to ascertain two key
elements: - The collected value: the total monetary
value of hats in a players TF2 inventory.
- The used value: monetary value of all hats equipped across all of their TF2 characters.
This collection of hats was cross-referenced against a third-party website for price listings
both for regular and 'unusual' hat types.
Players are grouped based on their status performance using what are known as clustering
techniques: a form of analysis that allows for grouping of data based on the features
of the dataset, resulting in tight-knit groups or clusters.
Lim adopted the k-means clustering algorithm to group players more accurately based upon
their status performance.
Meanwhile Tie Strength is related to the number of friends a person establishes on a social
network with respect to their activity.
Naturally, this varies depending on the social network itself and the nature with which connections
arise.
For example, the tie-strength between Facebook and Twitter will vary significantly given
the nature of the relationship between friends on each network and the manner in which user
activity is disseminated; with Facebook reliant largely on posting and commenting one anothers
wall while Twitter is a broadcast medium.
This is ascertained in Steam by calculating a number of key variables:
- User's number of friends and the length of their relationship on Steam.
- Posts on their own walls and the walls of their friends.
- The amount of words exchanged between wall posts.
- The number of virtual items the player has on Steam and specifically, the number of items
they have accrued through trading on the platform.
- The number of common applications: games that are shared among all of your friends
(typically those that you would play together).
- The number of positive and negative emotional words used in social interactions (measured
through sentiment analysis).
- Measuring the number of mutual friends and common groups among players.
Allowing for an understanding of social clusters that exist on the platform.
This data is naturally rather coarse, so it's then tidied up by running a statistical method
known as Principal Component Analysis to streamline it for future calculations by reducing the
data into key features it expresses.
This allows for the data to be discussed on a 'social status' level, rather than a level
of specific data ranges and constraints, which is pretty difficult to do and even harder
when trying to explain it all in a 10 minute YouTube video.
With all of this data acquired and reduced, the last part was to adopt a method of merging
them together: to be able to . To bring this together, Lim adopts Support Vector Machines:
and ultimately
With the status performance and social status identified, the next and critical component
was how to understand how these two elements interact with one another: can we predict
a status performance based on a player's social status?
This led to an analysis of how the clustered status performance groups (and the labels
that are associated with them) relate to the social status markers.
This required both sets of data to be appropriately clustered such that any relationships can
be properly mapped.
By clustering the social status data by also using k-means, two tests are made: first,
to understand whether *any* relationship exists between the two clusters, followed by a second
test to establish whether a specific social status could imply status performance.
This last part is achieved through use of Support Vector Machines (SVM): a form of supervised
machine learning that can be used for classification purposes.
In this instance it is used to train an appropriate classification model for prediction of player
behaviour.
With the data acquired from Steam, Lim was able to identify a broad range of monetary
value in the hats worn, ranging from hats worth nothing in a monetary sense, to some
worth over $1100.
With subsequent analysis, the monetary value of hats can be attributed to three status
performance clusters with players of lower status performance on-average wore hats worth
less than 25 cents.
Meanwhile high status performance shows players on-average wore hats valued at almost $200.
Interestingly, medium and high status-performance players had higher valued items on average
equipped versus in their inventory, meanwhile low status-performance players on-average
had more valuable items in their inventory.
So what does that mean?
Well it suggests that players interests and intentions have an impact on the hat usage:
with low status-performance players not really caring whether or not they're wearing cool
or interesting hats (even if they have them lying around) while high status-performance
place a strong emphasis on wearing high-value cosmetic items and showing off their bling.
Further principal component analysis against the dataset of player profiles was able to
establish four characteristics or social behaviours that occur on the Steam platform:
- Players can have high social interaction despite the games they have in common being
single-player games.
- Players can have both close relationships with specific players as well as - in the
context of the Steam - many friends.
- Players who typically trade with one another have a higher chance of engaging in social
discourse via Steam, both before and after transactions are completed.
- Players fluctuate between those that typically post on their own wall versus those who will
focus on the use of the walls to maintain contact with friends.
Now this all sounds rather common sense and is largely to be expected but the beauty of
it is that these conclusions are derived from the PCA analysis of the data.
In other words, these characteristics, which sound rather nominal, are being reached from
statistical analysis of the data provided, which is pretty freaking cool.
With this completed, a final clustering effort took place for player profiles that match
those specific behavioural traits.
The final results identify 5 specific clusters of players which relate their social status
to status performance and is summarised in the table shown.
It appears that the three status performance groups can be broken down into five player
profile clusters.
With group 1 focussed on the low status performance players - meaning you don't really care about
how prominent your performance is presented online, through to group 5 which is exclusively
medium to high status performance where the most valuable and flashiest of hats are worn.
In short, you could actually build a model for the relationship between how much people
chat with friends on Steam and the hats they wore in TF2.
A final classification analysis using support vector machines was capable of predicting
what groups you as a player could fit into with an accuracy of over 60% just by modelling
hats and Steam performance.
What's interesting about this type of work is the implications it presents: most notably
that there are strong relationships that can be found between a player’s real-world identity
and social behaviour with their virtual identity in Team Fortress 2.
This can be of real interest to designers of both games and social media platforms,
ensuring they provide adequate technologies to not only enable users but also to be wary
of the effects of any coupling between real-world and virtual identities that might occur.
It leaves scope for application of real world cosmetic marketing to be applied and build
parallels to real world phenomena: such as limited edition or design apparel or building
promotions with developers for cosmetic items.
But as noted in Lim's papers publishing this research, there are issues of social impact
and this type of behaviour could result in sociological issues such as perceived privilege
or margnialisation.
We've since seen evidence of promotions, exclusive content and the community frustration occur
in the likes of not just TF2 but also PayDay 2, Killer Instinct, Overwatch, Call of Duty:
Black Ops 3 and many other online games.
If anything, Lim's research foretold many of the current trends in cosmetic items in
online gaming, because the research shows that this can have a tangible effect on players
interactions and that ultimately, some players will place a great amount of attention on
their aesthetics.
Hey, I'm Tommy Thompson and I want to take a moment to thank you for watching this video
and please like and subscribe.
This piece has been long in development and a difficult one to put together, I hope you've
enjoyed it.
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