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

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

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

Robot control software endows robots with advanced capabilities for autonomous operation, such as navigation, object recognition or manipulation, in unstructured and dynamic environments. However, there is a steady need for more robust oper- ation, where robots should perform complex tasks by reliably exploiting these novel capabilities. Mission-level resilience is re- quired in the presence of component faults through failure recovery. To address this challenge, a novel self-adaptation framework based on functional knowledge for augmented autonomy is presented. A metacontroller is integrated on top of the robot control system, and it uses an explicit run-time model of the robot’s controller and its mission to adapt to operational changes. The model is grounded on a functional ontology that relates the robot’s mission with the robot’s architecture, and it is generated during the robot’s development from its engineering models. Advantages are discussed from both theoretical and practical viewpoints. An application example in a real autonomous mobile robot is provided. In this example, the generic metacontroller uses the robot’s functional model to adapt the control architecture to recover from a sensor failure.

A self-adaptation framework based on functional knowledge for augmented autonomy in robots. Carlos Hernández, Julita Bermejo-Alonso and Ricardo Sanz. Integrated Computer-Aided Engineering 2018, IOS Press

Article @ ASlab

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