Active Causal Structure Learning with Latent Variables: Towards Learning to Detour in Autonomous Robots

ArXi:2410.20894v2 Announce Type: replace Artificial General Intelligence (AGI) Agents and Robots must be able to cope with everchanging environments and tasks. They must be able to actively construct new internal causal models of their interactions with the environment when new structural changes take place in the environment. Thus, we claim that active causal structure learning with latent variables (ACSLWL) is a necessary component to build AGI agents and robots.