Planning with Generative Cognitive Maps

Planning relies on cognitive maps -- models that encode world structure given cognitive resource constraints. The problem of learning functional cognitive maps is shared by humans, animals and machines. However, we still lack a clear understanding of how people represent maps for planning, particularly when the goal is to support cost-efficient plans. We take inspiration from theory of compositional mental representations in cognitive science to propose GenPlan: a cognitively-grounded computational framework that models redundant structure in maps and saves planning cost through policy reuse. Our framework integrates (1) a Generative Map Module that infers generative compositional structure and (2) a Structure-Based Planner that exploits structural redundancies to reduce planning costs. We show that our framework closely aligns with human behaviour, suggesting that people approximate planning by piece-wise policies conditioned on world structure. We also show that our approach reduces the computational cost of planning while producing good-enough plans, and contribute a proof-of-concept implementation demonstrating how to build these principles into a working system.