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Living organisms need to acquire both cognitive maps for learning thestructure of the world and planning mechanisms able to deal with the challengesof navigating ambiguous environments. Although significant progress has beenmade in each of these areas independently, the best way to integrate them is anopen research question. In this paper, we propose the integration of astatistical model of cognitive map formation within an active inference agentthat supports planning under uncertainty. Specifically, we examine theclone-structured cognitive graph (CSCG) model of cognitive map formation andcompare a naive clone graph agent with an active inference-driven clone graphagent, in three spatial navigation scenarios. Our findings demonstrate thatwhile both agents are effective in simple scenarios, the active inference agentis more effective when planning in challenging scenarios, in which sensoryobservations provide ambiguous information about location.

Integrating cognitive map learning and active inference for planning in ambiguous environments