Biological nervous systems are created in a fundamentally different way ...
Models leveraging both visual and textual data such as Contrastive
Langu...
Biological nervous systems consist of networks of diverse, sophisticated...
Procedural Content Generation (PCG) algorithms provide a technique to
ge...
Recent work has shown that Large Language Models (LLMs) can be incredibl...
Biological systems are very robust to morphological damage, but artifici...
Deep generative models can automatically create content of diverse types...
Organisms in nature have evolved to exhibit flexibility in face of chang...
Inspired by cellular growth and self-organization, Neural Cellular Autom...
In contrast to deep reinforcement learning agents, biological neural net...
The discovery of complex multicellular organism development took million...
Materials with the ability to self-classify their own shape have the
pot...
In nature, the process of cellular growth and differentiation has lead t...
Random exploration is one of the main mechanisms through which reinforce...
Applying neural network (NN) methods in games can lead to various new an...
Generative Adversarial Networks (GANs) are a powerful indirect
genotype-...
In games, as well as many user-facing systems, adapting content to users...
Generalization to out-of-distribution (OOD) circumstances after training...
In this paper, we introduce a novel combination of Bayesian Models (BMs)...
Neural Cellular Automata (NCAs) have been proven effective in simulating...
Despite recent advances in object detection using deep learning neural
n...
Morphological regeneration is an important feature that highlights the
e...
The advent of artificial intelligence (AI) and machine learning (ML) bri...
This paper introduces EvoCraft, a framework for Minecraft designed to st...
Planning is a powerful approach to reinforcement learning with several
d...
Recent work has shown promising results using Hebbian meta-learning to s...
Human-computer image generation using Generative Adversarial Networks (G...
Procedural content generation in video games has a long history. Existin...
Recent progress in Game AI has demonstrated that given enough data from ...
We present a pilot study on crea.blender, a novel co-creative game desig...
This paper presents iNNK, a multiplayer drawing game where human
players...
Lifelong learning and adaptability are two defining aspects of biologica...
Recent procedural content generation via machine learning (PCGML) method...
Methods for dynamic difficulty adjustment allow games to be tailored to
...
An important goal in reinforcement learning is to create agents that can...
Generative Adversarial Networks (GANs) are proving to be a powerful indi...
Generative Adversarial Networks (GANs) are an emerging form of indirect
...
This paper reviews the field of Game AI, which not only deals with creat...
AI algorithms are not immune to biases. Traditionally, non-experts have
...
Evolutionary-based optimization approaches have recently shown promising...
The idea behind procedural content generation (PCG) in games is to creat...
Generative Adversarial Networks (GANs) have shown im-pressive results fo...
Artificial life simulations are an important tool in the study of ecolog...
Imitation Learning (IL) is a machine learning approach to learn a policy...
Neural architectures inspired by our own human cognitive system, such as...
Over the last few years, deep reinforcement learning (RL) has shown
impr...
Generative Adversarial Networks (GANs) are a machine learning approach
c...
Bio-hybrid systems---close couplings of natural organisms with
technolog...
Reward shaping allows reinforcement learning (RL) agents to accelerate
l...
This paper describes an approach that combines generative adversarial
ne...