Journal
MATHEMATICS
Volume 10, Issue 15, Pages -Publisher
MDPI
DOI: 10.3390/math10152646
Keywords
probabilistic graphical model; recursive neural network; density estimation; evolving environment; non-stationary environment; relational learning
Categories
Ask authors/readers for more resources
The paper introduces a machine capable of learning and decision-making in evolving structured environments, utilizing a probabilistic graphical model and a probabilistic variant of the recursive neural network.
A difficult and open problem in artificial intelligence is the development of agents that can operate in complex environments which change over time. The present communication introduces the formal notions, the architecture, and the training algorithm of a machine capable of learning and decision-making in evolving structured environments. These environments are defined as sets of evolving relations among evolving entities. The proposed machine relies on a probabilistic graphical model whose time-dependent latent variables undergo a Markov assumption. The likelihood of such variables given the structured environment is estimated via a probabilistic variant of the recursive neural network.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available