502 research outputs found
Advancing performability in playable media : a simulation-based interface as a dynamic score
When designing playable media with non-game orientation, alternative play scenarios to gameplay scenarios must be accompanied by alternative mechanics to game mechanics. Problems of designing playable media with non-game orientation are stated as the problems of designing a platform for creative explorations and creative expressions. For such design problems, two requirements are articulated: 1) play state transitions must be dynamic in non-trivial ways in order to achieve a significant level of engagement, and 2) pathways for players’ experience from exploration to expression must be provided. The transformative pathway from creative exploration to creative expression is analogous to pathways for game players’ skill acquisition in gameplay. The paper first describes a concept of simulation-based interface, and then binds that concept with the concept of dynamic score. The former partially accounts for the first requirement, the latter the second requirement. The paper describes the prototype and realization of the two concepts’ binding. “Score” is here defined as a representation of cue organization through a transmodal abstraction. A simulation based interface is presented with swarm mechanics and its function as a dynamic score is demonstrated with an interactive musical composition and performance
Bootstrapping Conditional GANs for Video Game Level Generation
Generative Adversarial Networks (GANs) have shown im-pressive results for
image generation. However, GANs facechallenges in generating contents with
certain types of con-straints, such as game levels. Specifically, it is
difficult togenerate levels that have aesthetic appeal and are playable atthe
same time. Additionally, because training data usually islimited, it is
challenging to generate unique levels with cur-rent GANs. In this paper, we
propose a new GAN architec-ture namedConditional Embedding Self-Attention
Genera-tive Adversarial Network(CESAGAN) and a new bootstrap-ping training
procedure. The CESAGAN is a modification ofthe self-attention GAN that
incorporates an embedding fea-ture vector input to condition the training of
the discriminatorand generator. This allows the network to model
non-localdependency between game objects, and to count objects. Ad-ditionally,
to reduce the number of levels necessary to trainthe GAN, we propose a
bootstrapping mechanism in whichplayable generated levels are added to the
training set. Theresults demonstrate that the new approach does not only
gen-erate a larger number of levels that are playable but also gen-erates fewer
duplicate levels compared to a standard GAN
Co-generation of game levels and game-playing agents
Open-endedness, primarily studied in the context of artificial life, is the
ability of systems to generate potentially unbounded ontologies of increasing
novelty and complexity. Engineering generative systems displaying at least some
degree of this ability is a goal with clear applications to procedural content
generation in games. The Paired Open-Ended Trailblazer (POET) algorithm,
heretofore explored only in a biped walking domain, is a coevolutionary system
that simultaneously generates environments and agents that can solve them. This
paper introduces a POET-Inspired Neuroevolutionary System for KreativitY
(PINSKY) in games, which co-generates levels for multiple video games and
agents that play them. This system leverages the General Video Game Artificial
Intelligence (GVGAI) framework to enable co-generation of levels and agents for
the 2D Atari-style games Zelda and Solar Fox. Results demonstrate the ability
of PINSKY to generate curricula of game levels, opening up a promising new
avenue for research at the intersection of procedural content generation and
artificial life. At the same time, results in these challenging game domains
highlight the limitations of the current algorithm and opportunities for
improvement.Comment: 7 pages, 5 figures, AIIDE 202
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