Ontology Engineering for the Autonomous Systems Domain
Saturday, 11 May 2013
We have a new book chapter on ontologies for autonomous systems.

The chapter Ontology Engineering for the Autonomous Systems Domain, by Julita Bermejo–Alonso, Ricardo Sanz, Manuel Rodríguez and Carlos Hernández has been published in the book Knowledge Discovery, Knowledge Engineering and Knowledge Management.

Ontologies provide a common conceptualisation that can be shared by all stakeholders in an engineering development process. They provide a good means to analyse the domain, allowing to separate descriptive from problem–solving knowledge. Our research programme on autonomous systems considered an ontology as the adequate mechanism to conceptualise the autonomous systems domain, and the software engineering techniques applied to such systems. This paper describes the ontological engineering process of such an ontology: OASys (Ontology for Autonomous Systems). Its development considered different stages: the specification of the requirements to be fulfilled by the ontology; the extraction of the actual features needed to implement the desired requirements; the conceptualisation phase with the design decisions to integrate the different domains, theories and techniques addressed by the ontological elements; and finally, the implementation of the ontology, which integrates both ontology engineering and software engineering approaches by using UML as the implementation language.

Last Updated ( Saturday, 11 May 2013 )
Sensores inteligentes y el futuro de las máquinas
Saturday, 04 May 2013
La incorporación de inteligencia artificial a los sensores de las máquinas permite el desarrollo de aplicaciones sofisticadas de monitorización y control que llevarán, eventualmente, a la construcción de máquinas auto-conscientes.
Last Updated ( Saturday, 04 May 2013 )
On the limitations of standard statistical modeling in biological systems: A full Bayesian approach
Thursday, 11 April 2013
Jaime Gomez Ramirez and Ricardo Sanz
Progress in Biophysics and Molecular Biology

One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist.


Ramirez, J. G. and Sanz, R. On the limitations of standard statistical modeling in biological systems: A full bayesian approach for biology. Progress in Biophysics and Molecular Biology.

Article @ Elsevier

Last Updated ( Friday, 12 April 2013 )
Approaches and Assumptions of Self-Programming in Achieving Artificial General Intelligence
Sunday, 03 February 2013
Kristinn R. Thórisson, Eric Nivel, Ricardo Sanz and Pei Wang
Journal of Artificial General Intelligence

This is an editor's introduction to a special issue of the Journal of Artificial General Intelligence on the topic of Self-Programming and Constructivist Methodologies for AGI


Thórisson, K. R., Nivel, E., Sanz, R., and Wang, P. (2012). Approaches and assumptions of self-programming in achieving artificial general intelligence. Journal of Artificial General Intelligence, 3(3):1–10.

Article @ AGI Journal

Last Updated ( Friday, 12 April 2013 )
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