How does complexity arise in evolution
Complexity
A new kind of science
Machine Learning
Ansatz for dynamical hierarchies
Artificial Life
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Self-assembling dynamical hierarchies
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Artificial Life
On-line motif detection in time series with SwiftMotif
Pattern Recognition
Time series analysis with multiple resolutions
Information Systems
Adaptive modularization of the MAPK signaling pathway using the multiagent paradigm
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
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In this position paper we present a concept to automatically simplify computational processes in large-scale self-organizing multi-agent simulations. The fundamental idea is that groups of agents that exhibit predictable interaction patterns are temporarily subsumed by higher order agents with behaviours of lower computational costs. In this manner, hierarchies of meta-agents automatically abstract large-scale systems involving agents with in-depth behavioural descriptions, rendering the process of upfront simplification obsolete that is usually necessary in numerical approaches. Abstraction hierarchies are broken down again as soon as they become invalid, so that the loss of valuable process information due to simplification is minimized. We describe the algorithm and the representation, we argue for its general applicability and potential power and we underline the challenges that will need to be overcome.