7 research outputs found
Neuroevolutionary constrained optimization for content creation
This paper presents a constraint-based procedural
content generation (PCG) framework used for the creation of
novel and high-performing content. Specifically, we examine
the efficiency of the framework for the creation of spaceship
design (hull shape and spaceship attributes such as weapon and
thruster types and topologies) independently of game physics
and steering strategies. According to the proposed framework,
the designer picks a set of requirements for the spaceship
that a constrained optimizer attempts to satisfy. The constraint
satisfaction approach followed is based on neuroevolution;
Compositional Pattern-Producing Networks (CPPNs) which
represent the spaceship’s design are trained via a constraintbased
evolutionary algorithm. Results obtained in a number
of evolutionary runs using a set of constraints and objectives
show that the generated spaceships perform well in movement,
combat and survival tasks and are also visually appealing.peer-reviewe
Generating map sketches for strategy games
How can a human and an algorithm productively collaborate on generating game content? In this paper, we try to answer this question in the context of
generating balanced and interesting low-resolution sketches for game levels. We
introduce six important criteria for successful strategy game maps, and present
map sketches optimized for one or more of these criteria via a constrained evolutionary algorithm. The sketch-based map representation and the computationally
lightweight evaluation methods are geared towards the integration of the evolutionary algorithm within a mixed-initiative tool, allowing for the co-creation of
game content by a human and an artificial designer.peer-reviewe
Enhancements to constrained novelty search : two-population novelty search for generating game content
Novelty search is a recent algorithm geared to explore search spaces without regard to objectives; minimal criteria novelty search is a variant of this algorithm for constrained search spaces. For large search spaces with multiple constraints, however, it is hard to find a set of feasible individuals that is both large and diverse. In this paper, we present two new methods of novelty search for constrained spaces, Feasible-Infeasible Novelty Search and Feasible-Infeasible Dual Novelty Search. Both algorithms keep separate populations of feasible and infeasible individuals, inspired by the FI-2pop genetic algorithm. These algorithms are applied to the problem of creating diverse and feasible game levels, representative of a large class of important problems in procedural content generation for games. Results show that the new algorithms under certain conditions can produce larger and more diverse sets of feasible strategy game maps than existing algorithms. However, the best algorithm is contingent on the particularities of the search space and the genetic operators used. It is also shown that the proposed enhancement of offspring boosting increases performance in all cases.The research is supported, in part, by the FP7 ICT project
SIREN (project no: 258453) and by the FP7 ICT project
C2Learn (project no: 318480).peer-reviewe
Adaptive game level creation through rank-based interactive evolution
This paper introduces Rank-based Interactive Evolution (RIE) which is an alternative to interactive evolution driven by computational models of user preferences to generate personalized content. In RIE, the computational models are adapted to the preferences of users which, in turn, are used as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using artificial agents. Results suggest that RIE is both faster and more robust than standard interactive evolution and outperforms other state-of-the-art interactive evolution approaches.The research is supported, in part, by the FP7 ICT project SIREN (project no: 258453) and by the FP7 ICT project C2Learn (project no: 318480).peer-reviewe
Adapting models of visual aesthetics for personalized content creation
This paper introduces a search-based approach to
personalized content generation with respect to visual aesthetics.
The approach is based on a two-step adaptation procedure
where (1) the evaluation function that characterizes the content
is adjusted to match the visual aesthetics of users and (2) the
content itself is optimized based on the personalized evaluation
function. To test the efficacy of the approach we design fitness
functions based on universal properties of visual perception,
inspired by psychological and neurobiological research. Using
these visual properties we generate aesthetically pleasing 2D
game spaceships via neuroevolutionary constrained optimization
and evaluate the impact of the designed visual properties on the
generated spaceships. The offline generated spaceships are used
as the initial population of an interactive evolution experiment in
which players are asked to choose spaceships according to their
visual taste: the impact of the various visual properties is adjusted
based on player preferences and new content is generated online
based on the updated computational model of visual aesthetics
of the player. Results are presented which show the potential of
the approach in generating content which is based on subjective
criteria of visual aesthetics.Thanks to all the participants of the interactive evolution
experiement. The research was supported, in part, by the
FP7 ICT project SIREN (project no: 258453) and by the
Danish Research Agency, Ministry of Science, Technology
and Innovation project AGameComIn; project number: 274-
09-0083.peer-reviewe
Neuroevolutionary Constrained Optimization for Content Creation
Abstract — This paper presents a constraint-based procedural content generation (PCG) framework used for the creation of novel and high-performing content. Specifically, we examine the efficiency of the framework for the creation of spaceship design (hull shape and spaceship attributes such as weapon and thruster types and topologies) independently of game physics and steering strategies. According to the proposed framework, the designer picks a set of requirements for the spaceship that a constrained optimizer attempts to satisfy. The constraint satisfaction approach followed is based on neuroevolution; Compositional Pattern-Producing Networks (CPPNs) which represent the spaceship’s design are trained via a constraintbased evolutionary algorithm. Results obtained in a number of evolutionary runs using a set of constraints and objectives show that the generated spaceships perform well in movement, combat and survival tasks and are also visually appealing. I