2015/08/26 by Moreira, Catarina, Wichert, Andreas
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences
paper · doi:10.48550/arxiv.1508.06973
We analyse a quantum-like Bayesian Network that puts together cause/effect relationships and semantic similarities between events. These semantic similarities constitute acausal connections according to the Synchronicity principle and provide new relationships to quantum like probabilistic graphical models. As a consequence, beliefs (or any other event) can be represented in vector spaces, in which quantum parameters are determined by the similarities that these vectors share between them. Events attached by a semantic meaning do not need to have an explanation in terms of cause and effect.