31 research outputs found
Evolving personas for player decision modeling
This paper explores how evolved game playing
agents can be used to represent a priori defined archetypical
ways of playing a test-bed game, as procedural personas. The
end goal of such procedural personas is substituting players
when authoring game content manually, procedurally, or both
(in a mixed-initiative setting). Building on previous work, we
compare the performance of newly evolved agents to agents
trained via Q-learning as well as a number of baseline agents.
Comparisons are performed on the grounds of game playing
ability, generalizability, and conformity among agents. Finally,
all agents’ decision making styles are matched to the decision
making styles of human players in order to investigate whether
the different methods can yield agents who mimic or differ
from human decision making in similar ways. The experiments
performed in this paper conclude that agents developed from
a priori defined objectives can express human decision making
styles and that they are more generalizable and versatile than
Q-learning and hand-crafted agents.peer-reviewe
MiniDungeons 2 : an experimental game for capturing and modeling player decisions
This paper describes MiniDungeons 2 (MD2): a turn-based rogue-like game developed to support research in capturing and modeling player decision making processes through procedural personas and using such models as critics for procedural content generation. MD2 intends to provide a full-circle framework for collecting, modeling, simulating, and
producing content for player decision making styles.
The fully instrumented and telemetric game will soon be
made available to the public to be played on smart-phones
for the purpose of collecting as many play traces, representing as many different decision making styles, as possible.peer-reviewe
The Personalization Paradox: the Conflict between Accurate User Models and Personalized Adaptive Systems
Personalized adaptation technology has been adopted in a wide range of
digital applications such as health, training and education, e-commerce and
entertainment. Personalization systems typically build a user model, aiming to
characterize the user at hand, and then use this model to personalize the
interaction. Personalization and user modeling, however, are often
intrinsically at odds with each other (a fact some times referred to as the
personalization paradox). In this paper, we take a closer look at this
personalization paradox, and identify two ways in which it might manifest:
feedback loops and moving targets. To illustrate these issues, we report
results in the domain of personalized exergames (videogames for physical
exercise), and describe our early steps to address some of the issues arisen by
the personalization paradox.Comment: arXiv admin note: substantial text overlap with arXiv:2101.1002
Personas versus clones for player decision modelling
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.peer-reviewe
Two-step constructive approaches for dungeon generation
This paper presents a two-step generative approach for creating dungeons in the rogue-like puzzle game MiniDungeons 2. Generation is split into two steps, initially producing the architectural layout of the level as its walls and floor tiles, and then furnishing it with game objects representing the player's start and goal position, challenges and rewards. Three layout creators and three furnishers are introduced in this paper, which can be combined in different ways in the two-step generative process for producing diverse dungeons levels. Layout creators generate the floors and walls of a level, while furnishers populate it with monsters, traps, and treasures. We test the generated levels on several expressivity measures, and in simulations with procedural persona agents.peer-reviewe