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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 )
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 )
An Apology for the “Self” Concept in Autonomous Robot Ontologies Print E-mail
Thursday, 11 April 2013
Ricardo Sanz, Julita Bermejo-Alonso, Claudio Rossi, Miguel Hernando, Koro Irusta and Esther Aguado
Presented at ROBOT 2019 Fourth Iberian Robotics Conference

The IEEE has working groups developing standards in many domains. One of these is the elaboration of an standard ontology for autonomous robots. An ontology is a specification of a conceptualization and is useful for having a common conceptual ground when interchanging data about systems. We are working of this standard based on our experience on autonomous systems construction.

This paper focuses on the core idea that underlies all mechanisms for system self-awareness: “Self”. Robot self awareness is a hot topic not only from a bioinspiration perspective but also from a more pro- found reflection-based strategy for increased autonomy and resilience. In this paper we address the uses and genealogy of the concept of “self”, its value in the implementation of robots and the role it may play in autonomous robotic systems’ architectures. We hence propose the inclusion of the “Self” concept in the future IEEE AuR standard ontology.

Article @ ASlab

Last Updated ( Sunday, 15 September 2019 )
Consciousness and Understanding in Autonomous Systems Print E-mail
Thursday, 11 April 2013
Ricardo Sanz and Julita Bermejo-Alonso
Presented at Towards Conscious AI Systems Symposium. AAAI Spring Symposium Series, Stanford University 2019

his position paper highlights the importance of having a formal notion of understanding as one of the cornerstones in the construction of conscious AIs. It will show that the capability of understanding both the perceptual and the action flows is critical for the correct operation of situated autonomous systems. An assessment is also made on the contribution of the machine learning domain towards this direction.

Article @ ASlab

Presentation @ ASlab

Last Updated ( Sunday, 15 September 2019 )
Biologically Inspired Cognitive Architectures 2011 Print E-mail
Sunday, 13 November 2011

The Biologically Inspired Cognitive Architectures 2011 Conference took place at Washington, USA on 4-6 November 2011.

The challenge of creating a real-life computational equivalent of the human mind calls for our joint efforts to better understand at a computational level how natural intelligent systems develop their cognitive and learning functions. BICA conference grew up from a AAAI Fall symposium, focusing on the emergent hot topics in computer, brain and cognitive sciences unified by the challenge of replicating the human mind in a computer.

In this event we presented a general model of emotion based on the interpretation of emotional processes as control reorganisations driven by values.

Concurrent control patterns deployed over neural components.

Adaptive systems use feedback as a key strategy to cope with uncertainty and change in their environments. The information fed back from the sensorimotor loop into the control architecture can be used to change different elements of the controller at four different levels: parameters of the control model, the control model itself, the functional organization of the agent and the functional components of the agent. The complexity of such a space of potential configurations is daunting. The only viable alternative for the agent –in practical, economical, evolutionary terms– is the reduction of the dimensionality of the configuration space.

This reduction is achieved both by functionalisation –or, to be more precise, by interface minimization– and by patterning, i.e. the selection among a predefined set of organisational configurations. This last analysis let us state the central problem of how autonomy emerges from the integration of the cognitive, emotional and autonomic systems in strict functional terms: autonomy is achieved by the closure of functional dependency.

This talk shows a general model of how the emotional biological systems operate following this theoretical analysis and how this model is also of applicability to a wide spectrum of artificial systems.

Get the slides of the talk.

Last Updated ( Sunday, 13 November 2011 )
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