SymboSLAM: Semantic Map Generation in a Multi-Agent System
This addresses the problem of solution transparency for human-machine interaction in multi-agent systems, though it appears incremental by applying symbolic reasoning to an existing domain.
The paper tackles the lack of explainability in environment-type classification and SLAM by proposing SymboSLAM, a symbolic method that uses ontological reasoning to generate semantically labeled maps, and it demonstrates effectiveness through evaluations on ground-truth maps in Canberra.
Sub-symbolic artificial intelligence methods dominate the fields of environment-type classification and Simultaneous Localisation and Mapping. However, a significant area overlooked within these fields is solution transparency for the human-machine interaction space, as the sub-symbolic methods employed for map generation do not account for the explainability of the solutions generated. This paper proposes a novel approach to environment-type classification through Symbolic Simultaneous Localisation and Mapping, SymboSLAM, to bridge the explainability gap. Our method for environment-type classification observes ontological reasoning used to synthesise the context of an environment through the features found within. We achieve explainability within the model by presenting operators with environment-type classifications overlayed by a semantically labelled occupancy map of landmarks and features. We evaluate SymboSLAM with ground-truth maps of the Canberra region, demonstrating method effectiveness. We assessed the system through both simulations and real-world trials.