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 )
The HBP Project @ RTVE
Sunday, 10 February 2013
Se ha concedido el proyecto HBP en el que participa la UPM en multiples ámbitos, incluyendo la neurorobótica por parte de nuestro grupo de investigación.

Last Updated ( Sunday, 10 February 2013 )
Consciousness, Action Selection, Meaning And Phenomenic Anticipation
Friday, 19 February 2010
Ricardo Sanz, Carlos Hernández, And M. G. Sánchez-Escribano
International Journal of Machine Consciousness

Phenomenal states are generally considered the ultimate sources of intrinsic motivation for autonomous biological agents. In this article, we will address the issue of the necessity of exploiting these states for the design and implementation of robust goal-directed artificial systems. We will provide an analysis of consciousness in terms of a precise definition of how an agent "understands" the informational flows entering the agent and its very own action possibilities. This abstract model of consciousness and understanding will be based in the analysis and evaluation of phenomenal states along potential future trajectories in the state space of the agents. This implies that a potential strategy to follow in order to build autonomous but still customer-useful systems is to embed them with the particular, ad hoc phenomenality that captures the system-external requirements that define the system usefulness from a customer-based, requirements-strict engineering viewpoint.


RICARDO SANZ, CARLOS HERNÁNDEZ, and M. G. SÁNCHEZ-ESCRIBANO, Int. J. Mach. Conscious. 04, 383 (2012).
DOI: 10.1142/S1793843012400227

Article @ IJMC

Last Updated ( Wednesday, 16 January 2013 )
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