A Neural Probabilistic Graphical Model for Learning and Decision Making in Evolving Structured Environments

Edmondo Trentin
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Edmondo Trentin: Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche (DIISM), Università di Siena, 53100 Siena, Italy

Mathematics, 2022, vol. 10, issue 15, 1-7

Abstract: 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.

Keywords: probabilistic graphical model; recursive neural network; density estimation; evolving environment; non-stationary environment; relational learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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