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Behavioral modeling based on Probabilistic Finite Automata: an empirical study

Abstract: Imagine an agent that performs tasks according to different strategies. The goal of Behavioral Recognition (BR) is to identify which of the available strategies is the one being used by the agent, by simply observing the agent's actions and the environmental conditions during a certain period of time. The goal of Behavioral Cloning (BC) is more ambitious. In this last case, the learner must be able to build a model of the behavior of the agent. In both settings, the only assumption is that the learner has access to a training set that contains instances of observed behavioral traces for each available strategy. This paper studies a machine learning approach based on Probabilistic Finite Automata (PFAs), capable of achieving both the recognition and cloning tasks. We evaluate the performance of PFAs in the context of a simulated learning environment (in this case, a virtual Roomba vacuum cleaner robot), and compare it with a collection of other machine learning approaches.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Fuente: Sensors, 2016, 16, 958

Editorial: MDPI

 Fecha de publicación: 01/06/2016

Nº de páginas: 16

Tipo de publicación: Artículo de Revista

 DOI: 10.3390/s16070958

ISSN: 1424-8220

Proyecto español: MTM2014-55262-P

Url de la publicación: https://doi.org/10.3390/s16070958