  Most of the current inference techniques rely upon Bayesian inference on
Probabilistic Graphical Models of observations and do predictions and
classification on observations. However, there is very little literature on the
mining of relationships between observations and building models of the
relationship between sets of observations or the generating context of the
observations. Moreover, event understanding of machines with observation inputs
needs to understand the relationship between observations. Thus there is a
crucial need to build models and develop effective data structures to
accumulate and organize relationships between observations. Given a PGM model,
this paper attempts to fit a permutation of states to a sequence of observation
tokens (The Travelling Photographer Problem). We have devised a machine
learning inspired architecture for randomized approximation of state
permutation, facilitating parallelization of heuristic search of permutations.
As a result, our algorithm can solve The Travelling Photographer Problem with
minimal error. Furthermore, we demonstrate that by mimicking machine learning
components such as normalization, dropout, and lambda layer with a randomized
algorithm, we can devise an architecture that solves TPP, a permutation NP-Hard
problem. Other than TPP, we can also provide a 2-Local improvement heuristic
for the Travelling Salesman Problem (TSP) with similar ideas.
