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Sensors 2016, 16(7), 958; doi:10.3390/s16070958

Behavioral Modeling Based on Probabilistic Finite Automata: An Empirical Study

1
Departamento de Matemáticas, Estadística y Computación, Universidad de Cantabria, Santander 39005, Spain
2
Department of Computer Science, Drexel University, Philadelphia, PA 19104, USA
3
Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
^This paper is an extended version of our paper published in Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information. Volume 9454 of the series Lecture Notes in Computer Science, 2015; pp. 103–115.
*
Author to whom correspondence should be addressed.
Academic Editors: Vladimir Villarreal and Carmelo R. García
Received: 28 April 2016 / Revised: 18 June 2016 / Accepted: 21 June 2016 / Published: 24 June 2016
(This article belongs to the Special Issue Selected Papers from UCAmI, IWAAL and AmIHEALTH 2015)
View Full-Text   |   Download PDF [769 KB, uploaded 24 June 2016]   |  

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. View Full-Text
Keywords: learning from observation; behavioral recognition; behavioral cloning; probabilistic finite automaton; ambient intelligence; virtual agents learning from observation; behavioral recognition; behavioral cloning; probabilistic finite automaton; ambient intelligence; virtual agents
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Tîrnăucă, C.; Montaña, J.L.; Ontañón, S.; González, A.J.; Pardo, L.M. Behavioral Modeling Based on Probabilistic Finite Automata: An Empirical Study. Sensors 2016, 16, 958.

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