Learning Paradigms for Multi-Agent Architectures to Solve Real World Tasks

This research proposes a novel approach to modeling multi-agent communication by leveraging mathematical structures called simplicial complexes. Unlike traditional graph-based approaches that primarily focus on pairwise interactions between agents, simplicial complexes can represent higher- order relationships that emerge when multiple agents interact simultaneously as groups. We propose a simulation environment similar to the board game “Clue” (Cleudo) to test which multi-agent architectures perform best when they need to process large volumes of relevant contextual information.