[2410.20894] Working Paper: Active Causal Structure Learning with Latent Variables: Towards Learning to Detour in Autonomous Robots

Computer Science > Artificial Intelligence arXiv:2410.20894 (cs) [Submitted on 28 Oct 2024 (v1), last revised 26 Mar 2026 (this version, v3)] Title:Working Paper: Active Causal Structure Learning with Latent Variables: Towards Learning to Detour in Autonomous Robots Authors:Pablo de los Riscos, Fernando J. Corbacho View a PDF of the paper titled Working Paper: Active Causal Structure Learning with Latent Variables: Towards Learning to Detour in Autonomous Robots, by Pablo de los Riscos and Fernando J. Corbacho View PDF HTML (experimental) Abstract: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. This paper describes how a complex planning and expectation-based detour behavior can be learned by ACSLWL when, unexpectedly, and for the first time, the simulated robot encounters a sort of transparent barrier in its pathway towards its target. ACSWL consists of acting in the environment, discovering new causal relations, constructing new causal models, exploiting the causal models to maximize its expected utility, detecting possible latent variables when unexpected observations occur, and constructing new ...