110 research outputs found
Designer modeling for sentient sketchbook
This paper documents the challenges in creating a
computer-aided level design tool which incorporates computergenerated
suggestions which appeal to the human user. Several
steps are suggested in order to make the suggestions more
appropriate to a specific user’s overall style, current focus, and
end-goals. Designer style is modeled via choice-based interactive
evolution which adapts the impact of different dimensions of
quality based on the designer’s choice of certain suggestions over
others. Modeling process is carried out similarly to style, but
adapting to the current focus of the designer’s actions. Goals are
modeled by estimating the visual patterns of the designer’s final
artifact and changing the parameters of the algorithm to enforce
such patterns on generated suggestions.The research is supported, in part, by the FP7 ICT project
C2Learn (project no: 318480) and the FP7 Marie Curie CIG
project AutoGameDesign (project no: 630665).peer-reviewe
Can computers foster human users' creativity? Theory and praxis of mixed-initiative co-creativity
This article discusses the impact of artificially intelligent computers to the process of design, play and educational activities. A computational process which has the necessary intelligence and creativity to take a proactive role in such activities can not only support human creativity but also foster it and prompt lateral thinking. The argument is made both from the perspective of human creativity, where the computational input is treated as an external stimulus which triggers re-framing of humans’ routines and mental associations, but also from the perspective of computational creativity where human input and initiative constrains the search space of the algorithm, enabling it to focus on specific possible solutions to a problem rather than globally search for the optimal. The article reviews four mixed-initiative tools (for design and educational play) based on how they contribute to human-machine co-creativity. These paradigms serve different purposes, afford different human interaction methods and incorporate different computationally creative processes. Assessing how co-creativity is facilitated on a per-paradigm basis strengthens the theoretical argument and provides an initial seed for future work in the burgeoning domain of mixed-initiative interaction.peer-reviewe
Can computers foster human users' creativity? Theory and praxis of mixed-initiative co-creativity
This article discusses the impact of artificially intelligent computers to the process of design, play and educational activities. A computational process which has the necessary intelligence and creativity to take a proactive role in such activities can not only support human creativity but also foster it and prompt lateral thinking. The argument is made both from the perspective of human creativity, where the computational input is treated as an external stimulus which triggers re-framing of humans’ routines and mental associations, but also from the perspective of computational creativity where human input and initiative constrains the search space of the algorithm, enabling it to focus on specific possible solutions to a problem rather than globally search for the optimal. The article reviews four mixed-initiative tools (for design and educational play) based on how they contribute to human-machine co-creativity. These paradigms serve different purposes, afford different human interaction methods and incorporate different computationally creative processes. Assessing how co-creativity is facilitated on a per-paradigm basis strengthens the theoretical argument and provides an initial seed for future work in the burgeoning domain of mixed-initiative interaction.peer-reviewe
Boosting computational creativity with human interaction in mixed-initiative co-creation tasks
Research in computational creativity often focuses on
autonomously creative systems, which incorporate creative
processes and result in creative outcomes. However,
the integration of artificially intelligent processes
in human-computer interaction tools necessitates that
we identify how computational creativity can be shaped
and ultimately enhanced by human intervention. This
paper attempts to connect mixed-initiative design with
established theories of computational creativity, and
adapt the latter to accommodate a human initiative
impacting computationally creative processes and outcomes.
Several case studies of mixed-initiative tools for
design and play are used to corroborate the arguments
in this paper.peer-reviewe
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
Sentient sketchbook : computer-assisted game level authoring
We would like to thank the participants of the user survey
for their valuable feedback.This paper introduces Sentient Sketchbook, a tool which
supports a designer in the creation of game levels. Us-
ing map sketches to alleviate designer effort, the tool auto-
mates playability checks and evaluations and visualizes sig-
ni cant gameplay properties. Most importantly, this paper
introduces constrained novelty search via a two-population
paradigm for generating, in real-time, alternatives to the
author's design and evaluates its potential against current
approaches. The paper concludes with a small-scale user
survey during which industry experts interact with Sentient
Sketchbook to design game levels. Results demonstrate the
tool's potential and provide directions for its improvement.peer-reviewe
Constrained novelty search : a study on game content generation
Novelty search is a recent algorithm geared toward exploring search spaces without regard to objectives. When the presence of constraints divides a search space into feasible space and infeasible space, interesting implications arise regarding how novelty search explores such spaces. This paper elaborates on the problem of constrained novelty search and proposes two novelty search algorithms which search within both the feasible and the infeasible space. Inspired by the FI-2pop genetic algorithm, both algorithms maintain and evolve two separate populations, one with feasible and one with infeasible individuals, while each population can use its own selection method. The proposed algorithms are applied to the problem of generating diverse but playable game levels, which is representative of the larger problem of procedural game content generation. Results show that the two-population constrained novelty search methods can create, under certain conditions, larger and more diverse sets of feasible game levels than current methods of novelty search, whether constrained or unconstrained. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. Additionally, the proposed enhancement of offspring boosting is shown to enhance performance in all cases of two-population novelty search.peer-reviewe
Learning the Designer's Preferences to Drive Evolution
This paper presents the Designer Preference Model, a data-driven solution
that pursues to learn from user generated data in a Quality-Diversity
Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the
user's design style to better assess the tool's procedurally generated content
with respect to that user's preferences. Through this approach, we aim for
increasing the user's agency over the generated content in a way that neither
stalls the user-tool reciprocal stimuli loop nor fatigues the user with
periodical suggestion handpicking. We describe the details of this novel
solution, as well as its implementation in the MI-CC tool the Evolutionary
Dungeon Designer. We present and discuss our findings out of the initial tests
carried out, spotting the open challenges for this combined line of research
that integrates MI-CC with Procedural Content Generation through Machine
Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European
Conference on the Applications of Evolutionary and bio-inspired Computation,
EvoApplications 202
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