  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.
