2 research outputs found
Mixed-initiative co-creativity
Creating and designing with a machine: do we merely create together (co-create) or can a machine truly foster our
creativity as human creators? When does such co-creation
foster the co-creativity of both humans and machines? This
paper investigates the simultaneous and/or iterative process
of human and computational creators in a mixed-initiative
fashion within the context of game design and attempts to
draw from both theory and praxis towards answering the
above questions. For this purpose, we first discuss the strong
links between mixed-initiative co-creation and theories of
human and computational creativity. We then introduce
an assessment methodology of mixed-initiative co-creativity
and, as a proof of concept, evaluate Sentient Sketchbook as a
co-creation tool for game design. Core findings suggest that
tools such as Sentient Sketchbook are not mere game authoring systems or mere enablers of creation but, instead, foster
human creativity and realize mixed-initiative co-creativity.peer-reviewe
Generative agents for player decision modeling in games
This paper presents a method for modeling player decision
making through the use of agents as AI-driven personas.
The paper argues that artificial agents, as generative player
models, have properties that allow them to be used as psychometrically valid, abstract simulations of a human player’s
internal decision making processes. Such agents can then be
used to interpret human decision making, as personas and
playtesting tools in the game design process, as baselines for
adapting agents to mimic classes of human players, or as believable, human-like opponents. This argument is explored
in a crowdsourced decision making experiment, in which the
decisions of human players are recorded in a small-scale dungeon themed puzzle game. Human decisions are compared
to the decisions of a number of a priori defined “archetypical”
agent-personas, and the humans are characterized by their
likeness to or divergence from these. Essentially, at each
step the action of the human is compared to what actions
a number of reinforcement-learned agents would have taken
in the same situation, where each agent is trained using a
different reward scheme. Finally, extensions are outlined for
adapting the agents to represent sub-classes found in the
human decision making traces.peer-reviewe