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monsters, avoid danger or get to the exit quickly). Using these affordances we trained
agents to play the game rationally for each objective. We call these agents procedural
personas. Both Q-learning [6] and evolutionary algorithms [5] were used to train high-
performing agents; the evolved agents have the benefit that they generalize to levels
they were not trained on. The agents' behavior was compared to play traces of the
human players through a metric we call the action agreement ratio (AAR) which
compares agents and humans at the action level. But is this really the right level of
analysis for comparing players to agents? It could be argued that the microscopic level
of comparing actions gives a biased view of how well an agent's behavior reproduces
player behavior, and that it is more interesting to look at behavior on the level of
conscious decisions. Further, are we right to assume boundedly rational behavior
given some set of objectives? It might be that with the same agent representation,
we could train agents that reproduce player behavior better by using the actual
playtraces as training data.
The current paper tries to answer these two questions. We propose a new
playtrace comparison method (tactical agreement ratio ) that instead of asking
whether an agent would perform the same action as the player in a given state
asks whether it would choose to pursue the same affordance in that state. We also
train agents to behave as similarly as possible to human players using play- traces
as objectives; we call such agents clones. Clones are compared to personas on
both seen and unseen levels, using both action-level and affordance-level com-
parison. In the following we briefly outline the relations between persona theory,
decision theory, player modeling, and the resulting concept of procedural per- sonas.
We briefly describe our testbed game, MiniDungeons, and the methods we used to
create game playing personas and clones, before we present the results from
comparing the resulting agents to the human players.
2
Related Work
In this section we outline our concept of procedural personas, relating it to its
roots in decision theory and the use of personas for game design.
Decision Theory and Games:
The personas used for expressing designer
notions of archetypical player behavior in MiniDungeons are structured around
the central concepts of decision theory. Decision theory states that whenever a
human makes a rational decision in a given situation, the decision is a result of
an attempt to optimize the expected utility [7]. Utility describes any positive out-
come for the decision maker and is fundamentally assumed to be idiosyncratic.
This means that in principle no definite assumptions can be made about what can
provide utility to the decision maker. The problem is further complicated by the
fact that the effort a decision maker directs toward attaining maximum util- ity
from a decision can be contingent on the expected utility itself. For problems that
are expected to provide low utility even in the best case, humans are prone to rely
more heavily on heuristics and biases for the decision making process [11, 4]. In
