Forecasting Local Behavior of Self-organizing Many-agent System without Reconstruction

TL;DR

This paper introduces a CNN-LSTM model that predicts the future state of a specific agent in a large self-organizing multi-agent system without reconstructing all agent states, reducing computational costs while maintaining accuracy.

Contribution

The paper presents a novel CNN-LSTM approach that forecasts individual agent behavior directly, avoiding full system reconstruction and lowering computational expenses.

Findings

Comparable or slightly worse AUC than reconstruction methods

Significantly reduced computational costs

Higher AUC with less computation than CNN-LSTM

Abstract

Large multi-agent systems are often driven by locally defined agent interactions, which is referred to as self-organization. Our primary objective is to determine when the propagation of such local interactions will reach a specific agent of interest. Although conventional approaches that reconstruct all agent states can be used, they may entail unnecessary computational costs. In this paper, we investigate a CNN-LSTM model to forecast the state of a particular agent in a large self-organizing multi-agent system without the reconstruction. The proposed model comprises a CNN encoder to represent the system in a low-dimensional vector, a LSTM module to learn agent dynamics in the vector space, and a MLP decoder to predict the future state of an agent. As an example, we consider a forest fire model where we aim to predict when a particular tree agent will start burning. We compare the proposed model with reconstruction-based approaches such as CNN-LSTM and ConvLSTM. The proposed model exhibits similar or slightly worse AUC but significantly reduces computational costs such as activation than ConvLSTM. Moreover, it achieves higher AUC with less computation than the recontruction-based CNN-LSTM.