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Towards Real-World Capable Spatial Memory
in the LIDA Cognitive Architecture
The ability to represent and utilize spatial information relevant to their goals is vital for intelligent
agents. Doing so in the real world presents significant challenges, which have so far
mostly been addressed by robotics approaches neglecting cognitive plausibility; whereas existing
cognitive models mostly implement spatial abilities in simplistic environments, neglecting
uncertainty and complexity. Here, we take a step towards computational software agents capable of forming spatial memories
in realistic environments, based on the biologically inspired LIDA cognitive architecture.
We identify and address challenges faced by agents operating with noisy sensors and actuators
in a complex physical world, including near-optimal integration of spatial cues from different
modalities for localization and mapping, correcting cognitive maps when revisiting locations,
the structuring of complex maps for computational efficiency, and multi-goal route planning
on hierarchical cognitive maps. We also describe computational mechanisms addressing these
challenges based on LIDA, and demonstrate their functionality by replicating several psychological
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