Files and links (1)
Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
Behavioral Modeling Based on Probabilistic Finite Automata: An Empirical Study
Sensors (Basel, Switzerland), v 16(7), pp 958-958
01 Jul 2016
PMID: 27347956
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.
Metrics
Details
- Title
- Behavioral Modeling Based on Probabilistic Finite Automata: An Empirical Study
- Creators
- Cristina Tirnauca - University of CantabriaJose L. Montana - University of CantabriaSantiago Ontanon - Drexel UniversityAvelino J. Gonzalez - University of Central FloridaLuis M. Pardo - University of Cantabria
- Publication Details
- Sensors (Basel, Switzerland), v 16(7), pp 958-958
- Publisher
- Mdpi
- Number of pages
- 16
- Grant note
- MTM2014-55262-P / project PAC::LFO of Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Ministerio de Ciencia e Innovacion (MICINN), Spain SCH-1521943 / National Science Foundation (NSF), USA; National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000380967000016
- Scopus ID
- 2-s2.0-84976292151
- Other Identifier
- 991019167895704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Chemistry, Analytical
- Engineering, Electrical & Electronic
- Instruments & Instrumentation