Abstraction of agent interaction processes: Towards large-scale multi-agent models

Abstract

The typically large numbers of interactions in agent-based simulations come at considerable computational costs. In this article, we present an approach to reduce the number of interactions based on behavioural patterns that recur during runtime. We employ machine learning techniques to abstract the behaviour of groups of agents to cut down computational complexity while preserving the inherent flexibility of agent-based models. The learned abstractions, which subsume the underlying model agents’ interactions, are constantly tested for their validity: after all, the dynamics of a system may change over time to such an extent that previously learned patterns would not reoccur. An invalid abstraction is, therefore, removed again from the system. The creation and removal of abstractions continues throughout the course of a simulation in order to ensure an adequate adaptation to the system dynamics. Experimental results on biological agent-based simulations show that our proposed approach can successfully reduce the computational complexity during the simulation while maintaining the freedom of arbitrary interactions.

Multiscale representation of simulated time

“…Although most applications of coarse-graining pertain to MD at extremely small scales, the underlying idea of treating several entities as one has been applied to somewhat larger-scale biological processes. Examples include multiscale models of blood clot formation developed using machine learning 21 or graph dynamical systems. 22…”