40 research outputs found
CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis
We propose and evaluate a new technique for learning hybrid automata
automatically by observing the runtime behavior of a dynamical system. Working
from a sequence of continuous state values and predicates about the
environment, CHARDA recovers the distinct dynamic modes, learns a model for
each mode from a given set of templates, and postulates causal guard conditions
which trigger transitions between modes. Our main contribution is the use of
information-theoretic measures (1)~as a cost function for data segmentation and
model selection to penalize over-fitting and (2)~to determine the likely causes
of each transition. CHARDA is easily extended with different classes of model
templates, fitting methods, or predicates. In our experiments on a complex
videogame character, CHARDA successfully discovers a reasonable
over-approximation of the character's true behaviors. Our results also compare
favorably against recent work in automatically learning probabilistic timed
automata in an aircraft domain: CHARDA exactly learns the modes of these
simpler automata.Comment: 7 pages, 2 figures. Accepted for IJCAI 201
Automated Game Design Learning
While general game playing is an active field of research, the learning of
game design has tended to be either a secondary goal of such research or it has
been solely the domain of humans. We propose a field of research, Automated
Game Design Learning (AGDL), with the direct purpose of learning game designs
directly through interaction with games in the mode that most people experience
games: via play. We detail existing work that touches the edges of this field,
describe current successful projects in AGDL and the theoretical foundations
that enable them, point to promising applications enabled by AGDL, and discuss
next steps for this exciting area of study. The key moves of AGDL are to use
game programs as the ultimate source of truth about their own design, and to
make these design properties available to other systems and avenues of inquiry.Comment: 8 pages, 2 figures. Accepted for CIG 201
Automatic Mapping of NES Games with Mappy
Game maps are useful for human players, general-game-playing agents, and
data-driven procedural content generation. These maps are generally made by
hand-assembling manually-created screenshots of game levels. Besides being
tedious and error-prone, this approach requires additional effort for each new
game and level to be mapped. The results can still be hard for humans or
computational systems to make use of, privileging visual appearance over
semantic information. We describe a software system, Mappy, that produces a
good approximation of a linked map of rooms given a Nintendo Entertainment
System game program and a sequence of button inputs exploring its world. In
addition to visual maps, Mappy outputs grids of tiles (and how they change over
time), positions of non-tile objects, clusters of similar rooms that might in
fact be the same room, and a set of links between these rooms. We believe this
is a necessary step towards developing larger corpora of high-quality
semantically-annotated maps for PCG via machine learning and other
applications.Comment: 9 pages, 7 figures. Appearing at Procedural Content Generation
Workshop 201
Tile Pattern KL-Divergence for Analysing and Evolving Game Levels
This paper provides a detailed investigation of using the Kullback-Leibler
(KL) Divergence as a way to compare and analyse game-levels, and hence to use
the measure as the objective function of an evolutionary algorithm to evolve
new levels. We describe the benefits of its asymmetry for level analysis and
demonstrate how (not surprisingly) the quality of the results depends on the
features used. Here we use tile-patterns of various sizes as features.
When using the measure for evolution-based level generation, we demonstrate
that the choice of variation operator is critical in order to provide an
efficient search process, and introduce a novel convolutional mutation operator
to facilitate this. We compare the results with alternative generators,
including evolving in the latent space of generative adversarial networks, and
Wave Function Collapse. The results clearly show the proposed method to provide
competitive performance, providing reasonable quality results with very fast
training and reasonably fast generation.Comment: 8 pages plus references. Proceedings of GECCO 201
Tabletop Roleplaying Games as Procedural Content Generators
Tabletop roleplaying games (TTRPGs) and procedural content generators can
both be understood as systems of rules for producing content. In this paper, we
argue that TTRPG design can usefully be viewed as procedural content generator
design. We present several case studies linking key concepts from PCG research
-- including possibility spaces, expressive range analysis, and generative
pipelines -- to key concepts in TTRPG design. We then discuss the implications
of these relationships and suggest directions for future work uniting research
in TTRPGs and PCG.Comment: 9 pages, 2 figures, FDG Workshop on Procedural Content Generation
202
Exploring Level Blending across Platformers via Paths and Affordances
Techniques for procedural content generation via machine learning (PCGML)
have been shown to be useful for generating novel game content. While used
primarily for producing new content in the style of the game domain used for
training, recent works have increasingly started to explore methods for
discovering and generating content in novel domains via techniques such as
level blending and domain transfer. In this paper, we build on these works and
introduce a new PCGML approach for producing novel game content spanning
multiple domains. We use a new affordance and path vocabulary to encode data
from six different platformer games and train variational autoencoders on this
data, enabling us to capture the latent level space spanning all the domains
and generate new content with varying proportions of the different domains.Comment: 6 pages, 5 figures, 16th AAAI Conference on Artificial Intelligence
and Interactive Digital Entertainment (AIIDE 2020
Interactive Evolution and Exploration within Latent Level-Design Space of Generative Adversarial Networks
Generative Adversarial Networks (GANs) are an emerging form of indirect
encoding. The GAN is trained to induce a latent space on training data, and a
real-valued evolutionary algorithm can search that latent space. Such Latent
Variable Evolution (LVE) has recently been applied to game levels. However, it
is hard for objective scores to capture level features that are appealing to
players. Therefore, this paper introduces a tool for interactive LVE of
tile-based levels for games. The tool also allows for direct exploration of the
latent dimensions, and allows users to play discovered levels. The tool works
for a variety of GAN models trained for both Super Mario Bros. and The Legend
of Zelda, and is easily generalizable to other games. A user study shows that
both the evolution and latent space exploration features are appreciated, with
a slight preference for direct exploration, but combining these features allows
users to discover even better levels. User feedback also indicates how this
system could eventually grow into a commercial design tool, with the addition
of a few enhancements.Comment: GECCO 202