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This paper proposes SIHA, a method for inferring interaction structures in multi-agent systems when agents are hidden, combining structure-agnostic initialization with iterative refinement to improve structural inference and trajectory reconstruction.
This paper models early language acquisition as a search on a graph-based mental lexicon using spreading activation and category exploration, outperforming a shortest path baseline in simulating normative word acquisition across four languages.