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practice, however, for structured, well-defined problems, insights from e.g.
psychology or contextual information about the decision maker or the de- cision
problem may provide us with opportunities for assuming which decisions are
important and which outcomes may be of utility to the decision maker. As
decision spaces, most games are special cases since the available decisions and
their consequences are highly structured by the game's mechanics and evaluation
mechanisms. Games, through their design, often provide specific affordances [3] to
the player, and suggest utility for various outcomes. This perspective forms the basis
for our understanding of player behavior in our testbed game, as we assume
that players are interacting with the game in accordance with the rules,
understanding and responding to the affordances of our game. This, in turn, mo-
tivates our use of utility for attaining game rule based affordances as the defining
characteristics of the personas we develop. Similar theoretical perspectives have
been described by other authors, notably Dave Mark in [8].
When attempting to characterize player decision making styles in games using
utilities, it is important to consider the level of decision making relevant for the
game, as described in [1]. Here, we model players at both the individual action level
as well as at the more tactical level of game affordances. Below we describe how
we apply simple utility based agents by using linear combinations of utilities to
define personas that represent archetypical decision making styles in our testbed
game at two levels of abstraction.
Player Modeling:
The concept of personas was first adapted to the domain
of (digital) games under the headline of play-personas by Canossa and Drachen
who define play-personas as “clusters of preferential interaction (what) and nav-
igation (where) attitudes, temporally expressed (when), that coalesce around
different kinds of inscribed affordances in the artefacts provided by game de-
signers” [2]. Our long term research agenda is to operationalize the play-persona
concept into actual game playing procedural personas, by building generative
models of player behavior from designer metaphors, actual play data, or combi-
nations of the two.
Generative models of player behavior can be learned using a number of dif-
ferent methods. A key dichotomy in any player modeling approach lies in the
influence of theory (vs. data) for the construction of the player model [15]. On one
end, model-based approaches rely on a theoretical framework (in our case per-
sona theory or expert domain knowledge) and on the other hand, computational
models are built in a model-free, data-driven fashion. In this paper, personas
represent the model-based approach while what we term clones represent the
data-driven approach. Model-free player modeling can be done by imitating the
player directly, using supervised learning methods on the playtraces, or indirectly
using some form of reinforcement learning to train agents to behave in a way that
agrees with high-level features extracted from the playtraces [12]. Evolutionary
computation can be used to optimize an agent to behave similarly to a playtrace
or optimize it to exhibit the same macro-properties as said playtrace [9, 12, 14].
Direct imitation is prone to a form of overfitting where the agent only learns to
cope with situations which exist in the playtraces, and might behave erratically
