Learning World Models With Hierarchical Temporal Abstractions: A Probabilistic Perspective

arXiv:2404.16078v1 Announce Type: cross
Abstract: Machines that can replicate human intelligence with type 2 reasoning capabilities should be able to reason at multiple levels of spatio-temporal abstractions and scales using internal world models. Devising formalisms to develop such internal world models, which accurately reflect the causal hierarchies inherent in the dynamics of the real world, is a critical research challenge in the domains of artificial intelligence and machine learning. This thesis identifies several limitations with the prevalent use of state …

abstract abstractions arxiv capabilities causal cs.ai cs.lg hierarchical human human intelligence intelligence machines multiple perspective reason reasoning replicate temporal type world world models