• Corpus ID: 221319487

Say "Sul Sul!" to SimSim, A Sims-Inspired Platform for Sandbox Game AI

@inproceedings{Charity2020SayS,
  title={Say "Sul Sul!" to SimSim, A Sims-Inspired Platform for Sandbox Game AI},
  author={Megan Charity and Dipika Rajesh and Rachel Ombok and Lisa B. Soros},
  booktitle={AIIDE},
  year={2020}
}
This paper proposes environment design in the life simulation game The Sims as a novel platform and challenge for testing divergent search algorithms. In this domain, which includes a minimal viability criterion, the goal is to furnish a house with objects that satisfy the physical needs of a simulated agent. Importantly, the large number of objects available to the player (whether human or automated) affords a wide variety of solutions to the underlying design problem. Empirical studies in a… 
View With Semantic Reader
2 Citations
Methods Citations
2

Figures and Tables from this paper

2 Citations

Exploring open-ended gameplay features with Micro RollerCoaster Tycoon
TLDR
Results indicate that building from scratch with no costs results in the widest diversity of high-performing designs.
Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials
TLDR
It is shown that the generated maps can strongly encourage or discourage different persona-like behaviors and range from simple solutions to complex puzzle-levels, making them perfect candidates for a tutorial generative system.

References

SHOWING 1-10 OF 27 REFERENCES
Talakat: bullet hell generation through constrained map-elites
We describe a search-based approach to generating new levels for bullet hell games, which are action games characterized by and requiring avoidance of a very large amount of projectiles. Levels are…
Using Fractal Neural Networks to Play SimCity 1 and Conway's Game of Life at Variable Scales
We introduce gym-city, a Reinforcement Learning environment that uses SimCity 1's game engine to simulate an urban environment, wherein agents might seek to optimize one or a combination of any…
Empowering Quality Diversity in Dungeon Design with Interactive Constrained MAP-Elites
TLDR
This work proposes the use of quality-diversity algorithms for mixed-initiative game content generation, and uses the MAP-Elites algorithm, an illumination algorithm which divides the population into a number of cells depending on their values along several behavioral dimensions.
Voxelbuild: a minecraft-inspired domain for experiments in evolutionary creativity
TLDR
A new domain inspired by the popular Minecraft video game featuring a larger behavior space that is substantially more difficult to exhaust is introduced, showcasing sample block structures built by evolved neural network controllers.
Constrained Novelty Search: A Study on Game Content Generation
TLDR
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.
POET: open-ended coevolution of environments and their optimized solutions
TLDR
The Paired Open-Ended Trailblazer (POET) algorithm is introduced, which generates diverse and sophisticated behaviors that create and solve a wide range of environmental challenges, many of which cannot be solved by direct optimization, or by a direct-path curriculum-building control algorithm.
Mapping hearthstone deck spaces through MAP-elites with sliding boundaries
TLDR
A novel modification of the MAP-Elites algorithm with Sliding Boundaries (MESB) is introduced and applies it to the design and rebalancing of Hearthstone, a popular collectible card game chosen for its number of multidimensional behavior features relevant to particular styles of play.
Procedural Content Generation through Quality Diversity
TLDR
In the last few years, a handful of applications of QD to procedural content generation and game playing have been proposed; this work discusses these and proposes challenges for future work.
Minimal criterion coevolution: a new approach to open-ended search
TLDR
This paper investigates the extent to which interactions between two coevolving populations, both subject to their own constraint, or minimal criterion, can produce results that are both functional and diverse even without any behavior characterization or novelty archive.
Evolving a diversity of virtual creatures through novelty search and local competition
TLDR
The results in an experiment evolving locomoting virtual creatures show that novelty search with local competition discovers more functional morphological diversity within a single run than models with global competition, which are more predisposed to converge.
...
...