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Large multi-agent systems are often driven by locally defined agentinteractions, which is referred to as self-organization. Our primary objectiveis to determine when the propagation of such local interactions will reach aspecific agent of interest. Although conventional approaches that reconstructall 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 aparticular agent in a large self-organizing multi-agent system without thereconstruction. The proposed model comprises a CNN encoder to represent thesystem in a low-dimensional vector, a LSTM module to learn agent dynamics inthe vector space, and a MLP decoder to predict the future state of an agent. Asan example, we consider a forest fire model where we aim to predict when aparticular tree agent will start burning. We compare the proposed model withreconstruction-based approaches such as CNN-LSTM and ConvLSTM. The proposedmodel exhibits similar or slightly worse AUC but significantly reducescomputational costs such as activation than ConvLSTM. Moreover, it achieveshigher AUC with less computation than the recontruction-based CNN-LSTM.

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