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Personas versus Clones for Player Decision
Modeling
Christoffer Holmgård
1
, Antonios Liapis
1
,
Julian Togelius
1
, a n d Georgios N.Yannakakis
1
,
2
1
Center for Computer Games Research, IT University of Copenhagen, Denmark
{holmgard,anli}@itu.dk,julian@togelius.com
2
Institute for Digital Games, University of Malta, Malta
georgios.yannakakis@um.edu.mt
Abstract.
The current paper investigates how to model human play
styles. Building on decision and persona theory we evolve game playing
agents representing human decision making styles. Two methods are
developed, applied, and compared: procedural personas, based on utilities
designed with expert knowledge, and clones, trained to reproduce play
traces. Additionally, two metrics for comparing agent and human
decision making styles are proposed and compared. Results indicate that
personas evolved from designer intuitions can capture human decision
making styles equally well as clones evolved from human play traces.
1
Introduction
The current paper investigates how to create generative models of human player
behavior or playing style in games. This can be seen as a method for under-
standing game-playing behavior. Generative models of playing behavior are also
potentially useful in procedural play testing for procedural content generation,
for simulation based testing, and within mixed-initiative game design tools for
instant feedback during the design process. In other words, agents that play like
humans can help understand content as it is being created, by playing it. This
paper assumes that game players exhibit bounded rationality, i.e. they play to
optimize some objective or set of objectives, but that they might not be very good
at it. Playing style could then be characterized by how the players' in-game
decisions differ from those of an agent that played rationally (given some set of
objectives). We investigate this by using AI methods to train agents that behave
rationally, and see to what extent they can predict human players' behaviors.
In previous work we have designed a simple turn-based, tile-based rogue like game
which features monsters, treasures and potions in mazes [6]. 38 players played 10
levels of this game, and we recorded their every action. Next, we analyzed the design of
the game to extract a number of possible affordances which we translated into
partially conflicting objectives that a player might seek to fulfill (e.g. kill all
