Testing our hypothesis using novelty search with local competition, a quality-diversity evolutionary algorithm that can increase visual diversity while maintaining quality in the form of adherence to the semantic prompt, we explore how different notions of visual diversity can affect both the process and the product of the algorithm.
Stochastic gradient descent (SGD) is a premium optimization method for training neural networks, especially for learning objectively defined labels such as image objects and events.
Our Go-Explore implementation not only introduces a new paradigm for affect modeling; it empowers believable AI-based game testing by providing agents that can blend and express a multitude of behavioral and affective patterns.
In this paper we build on recent advances in contrastive learning and showcase its benefits for representation learning in games.
Ranked #1 on Image Classification on Sports10
A core challenge of evolutionary search is the need to balance between exploration of the search space and exploitation of highly fit regions.
This paper introduces ARCH-Elites, a MAP-Elites implementation that can reconfigure large-scale urban layouts at real-world locations via a pre-trained surrogate model instead of costly simulations.
This paper introduces a surrogate model of gameplay that learns the mapping between different game facets, and applies it to a generative system which designs new content in one of these facets.
This paper contends that collaborative board games pose a different challenge to artificial intelligence as it must balance short-term risk mitigation with long-term winning strategies.
The system also proactively designs alternatives to the level and class pairing, and presents them to the designer as suggestions that improve the predicted balance of the game.
As of 2020, the international workshop on Procedural Content Generation enters its second decade.
While artificial intelligence has been applied to control players' decisions in board games for over half a century, little attention is given to games with no player competition.
What if emotion could be captured in a general and subject-agnostic fashion?
Tabletop roleplaying games (TTRPGs) and procedural content generators can both be understood as systems of rules for producing content.
In this study into the player's emotional theory of mind of gameplaying agents, we investigate how an agent's behaviour and the player's own performance and emotions shape the recognition of a frustrated behaviour.
Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics.
This paper presents a two-step generative approach for creating dungeons in the rogue-like puzzle game MiniDungeons 2.
Is it possible to predict the motivation of players just by observing their gameplay data?
Quality diversity is a recent family of evolutionary search algorithms which focus on finding several well-performing (quality) yet different (diversity) solutions with the aim to maintain an appropriate balance between divergence and convergence during search.
Maximalism in art refers to drawing on and combining multiple different sources for art creation, embracing the resulting collisions and heterogeneity.
This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas.
Inspired by the notion of surprise for unconventional discovery we introduce a general search algorithm we name surprise search as a new method of evolutionary divergent search.
It is clear that the current attempts at using algorithms to create artificial neural networks have had mixed success at best when it comes to creating large networks and/or complex behavior.