Evolving Structures in Complex Systems
In this paper we propose an approach for measuring growth of complexity of emerging patterns in complex systems such as cellular automata. We discuss several ways how a metric for measuring the complexity growth can be defined. This includes approaches based on compression algorithms and artificial neural networks. We believe such a metric can be useful for designing systems that could exhibit open-ended evolution, which itself might be a prerequisite for development of general artificial intelligence. We conduct experiments on 1D and 2D grid worlds and demonstrate that using the proposed metric we can automatically construct computational models with emerging properties similar to those found in the Conway's Game of Life, as well as many other emergent phenomena. Interestingly, some of the patterns we observe resemble forms of artificial life. Our metric of structural complexity growth can be applied to a wide range of complex systems, as it is not limited to cellular automata.
Code (1)
Tasks
Artificial LifeSimilar Papers 제목 키워드 기반
Wormhole: Concept-Aware Deep Representation Learning for Co-Evolving Sequences
Identifying and understanding dynamic concepts in co-evolving sequences is crucial for analyzing complex systems such as IoT applications, financial markets, and online activity logs. These concepts provide valuable insi…
Decision MakingRepresentation LearningClustering Time-Evolving Networks Using the Spatio-Temporal Graph Laplacian
Time-evolving graphs arise frequently when modeling complex dynamical systems such as social networks, traffic flow, and biological processes. Developing techniques to identify and analyze communities in these time-varyi…
ClusteringA Deep Autoregressive Model for Dynamic Combinatorial Complexes
We introduce DAMCC (Deep Autoregressive Model for Dynamic Combinatorial Complexes), the first deep learning model designed to generate dynamic combinatorial complexes (CCs). Unlike traditional graph-based models, CCs cap…
modelRethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecurity
As cyber-physical systems grow increasingly interconnected and spatially distributed, ensuring their resilience against evolving cyberattacks has become a critical priority. Spatio-Temporal Anomaly detection plays an imp…
Anomaly DetectionGraph LearningAdaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream
Online anomaly detection from a data stream is critical for the safety and security of many applications but is facing severe challenges due to complex and evolving data streams from IoT devices and cloud-based infrastru…
Anomaly Detection