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Ontologies as Backbone of Cognitive Systems Engineering Print E-mail
Friday, 10 March 2017
Ricardo Sanz, Julita Bermejo, Juan Morago and Carlos Hernández
INCOSE Cognition And OntologieS (CAOS) 2017

Cognitive systems are starting to be deployed as appliances across the technological landscape of modern societies. The increasing availability of high performance computing platforms has opened an opportunity for statistics-based cognitive systems that per- form quite as humans in certain tasks that resisted the symbolic methods of classic artificial intelligence. Cognitive artefacts appear every day in the media, raising a wave of mild fear concerning artificial intelligence and its impact on society. These systems, performance notwithstanding, are quite brittle and their reduced dependability limits their potential for massive deployment in mission-critical applications —e.g. in autonomous driving or medical diagnosis. In this paper we explore the actual possibility of building cognitive systems using engineering-grade methods that can assure the satisfaction of strict requirements for their operation. The final conclusion will be that, besides the potential improvement provided by a rigorous engineering process, we are still in need of a solid theory —possibly the main outcome of cognitive science— that could sustain such endeavour. In this sense, we propose the use of formal ontologies as back- bones of cognitive systems engineering processes and workflows.


Ontologies as Backbone of Cognitive Systems Engineering. Ricardo Sanz, Julita Bermejo, Juan Morago and Carlos Hernández. AISB Symposium on Cognition And OntologieS (CAOS) 2017

Draft paper @ ASLab

Last Updated ( Saturday, 11 March 2017 )
 
Ontology Engineering for the Autonomous Systems Domain Print E-mail
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 )
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On the limitations of standard statistical modeling in biological systems: A full Bayesian approach Print E-mail
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.

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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 )
 
Bounded Recursive Self-Improvement Print E-mail
Thursday, 11 April 2013
E. Nivel, K. R. Thórisson, B. R. Steunebrink, H. Dindo, G. Pezzulo, M. Rodriguez, C. Hernandez, D. Ognibene, J. Schmidhuber, R. Sanz, H. P. Helgason, A. Chella, G. K. Jonsson
To appear (Submitted on 24 Dec 2013)

We have designed a machine that becomes increasingly better at behaving in underspecified circumstances, in a goal-directed way, on the job, by modeling itself and its environment as experience accumulates. Based on principles of autocatalysis, endogeny, and reflectivity, the work provides an architectural blueprint for constructing systems with high levels of operational autonomy in underspecified circumstances, starting from a small seed. Through value-driven dynamic priority scheduling controlling the parallel execution of a vast number of reasoning threads, the system achieves recursive self-improvement after it leaves the lab, within the boundaries imposed by its designers. A prototype system has been implemented and demonstrated to learn a complex real-world task, real-time multimodal dialogue with humans, by on-line observation. Our work presents solutions to several challenges that must be solved for achieving artificial general intelligence.


Bounded Recursive Self-Improvement. E. Nivel, K. R. Thórisson, B. R. Steunebrink, H. Dindo, G. Pezzulo, M. Rodriguez, C. Hernandez, D. Ognibene, J. Schmidhuber, R. Sanz, H. P. Helgason, A. Chella, G. K. Jonsson

Article @ arXiv

Last Updated ( Saturday, 28 December 2013 )
 
Emotions and the engineering of adaptiveness in complex systems Print E-mail
Thursday, 11 April 2013
M.G. Sánchez-Escribano & R. Sanz
INCOSE Conference on Systems Engineering Research (CSER 2014)

A major challenge when building complex and critical systems is the management of change in the system and in its operational environment. The increasing complexity forces autonomous systems to detect critical changes to avoid their progress towards undesirable states. We need new methods to build systems that can tune their adaptability protocols, transferring the control of uncertainty to their inner domain to strive for wellness. In essence, these are mechanisms to impose the fulfillment of system-wide wellness requirements to reduce the influence of the outer domain to be fully driven by the influence of the inner one. From the stance of cognitive systems, biological emotion suggests a strategy to configure value-based systems to use semantic self-representations of the state. A method inspired by emotion theories can causally connect the inner domain of the system and its objectives of wellness, focusing on dynamically adapting the system to avoid the progress of critical states. This method shall endow the system with a transversal mechanism to monitor its inner processes, detecting critical states and managing its adaptivity in order to maintain the wellness goals. The paper describes the current vision produced by this work-in-progress.


Emotions and the engineering of adaptiveness in complex systems. M.G. sanchez-Escribano & R. Sanz. INCOSE Conference on Systems Engineering Research (CSER 2014)

Draft paper @ ASLab

Last Updated ( Saturday, 25 January 2014 )
 
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