Autonomous systems are expected to maintain a dependable operation without human intervention. They are intended to fulfill the mission for which they were deployed, properly handling the disturbances that may affect them. Underwater robots, such as the UX-1 mine explorer developed in the UNEXMIN project, are paradigmatic examples of this need. Underwater robots are affected by both external and internal disturbances that hamper their capability for autonomous operation. Long-term autonomy requires not only the capability of perceiving and properly acting in open environments but also a sufficient degree of robustness and resilience so as to maintain and recover the operational functionality of the system when disturbed by unexpected events. In this article, we analyze the operational conditions for autonomous underwater robots with a special emphasis on the UX-1 miner explorer. We then describe a knowledge-based self-awareness and metacontrol subsystem that enables the autonomous reconfiguration of the robot subsystems to keep mission-oriented capability. This resilience augmenting solution is based on the deep modeling of the functional architecture of the autonomous robot in combination with ontological reasoning to allow self-diagnosis and reconfiguration during operation. This mechanism can transparently use robot functional redundancy to ensure mission satisfaction, even in the presence of faults.

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Known attempts to build autonomous robots rely on complex control architectures, often implemented with the Robot Operating System platform (ROS). The implementation of adaptable architectures is very often ad hoc, quickly gets cumbersome and expensive. Reusable solutions that support complex, runtime reasoning for robot adaptation have been seen in the adoption of ontologies. While the usage of ontologies significantly increases system reuse and maintainability, it requires additional effort from the application developers to translate requirements into formal rules that can be used by an ontological reasoner. In this paper, we present a design tool that facilitates the specification of reconfigurable robot skills. Based on the specified skills, we generate corresponding runtime models for self-adaptation that can be directly deployed to a running robot that uses a reasoning approach based on ontologies. We demonstrate the applicability of the tool in a real robot performing a patrolling mission at a university campus.

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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 operation, where robots should perform complex tasks by reliably exploiting these novel capabilities. Mission-level resilience is required 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.@en

Nowadays we find ourselves in what is called the fourth industrial revolution: Industry 4.0. This revolution is being fostered in many countries to get a more competitive industry. Industry 4.0 target is to make more efficient and flexible plants, reduce times and costs of process and products lifecycle. Under this framework, models appear as a core component in every new development. Model-Based Systems Engineering is a methodology that allows for traceability and guarantees model consistency covering the entire lifecycle of the system. The objective of this work is to develop a holistic model of a process plant. Such model will be available during the entire lifecycle of the system (process + product) communicating in every stage with the specific (existing) tools used to develop it. In this paper, we present the development of the SysML model of a process plant (the production of ethylbenzene from benzene and ethylene), the architecture and the methodology to transform the central model to application-specific ones. We also explain how to execute these specific models from the core model keeping consistency between models and data.@en

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 perform 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 limips 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 backbones of cognitive systems engineering processes and workflows.

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Our research focuses on engineering processes for autonomous intelligent systems construction with a life-cycle holistic view, by means of a model-based framework. The conceptual core of the framework is ontologically-driven. Our ontological approach consists of two elements. The first one is a domain Ontology for Autonomous Systems (OASys) to capture the autonomous system structure, function and behaviour. The second element is an Ontology-driven Engineering Methodology (ODEM) to develop the target autonomous system. This methodology is based on Model-based Systems Engineering and produces models of the system as core assets. These models are used through the whole system life-cycle, from implementation or validation to operation and maintenance. On the application side, the ontological framework has been used to develop a metacontrol engineering technology for autonomous systems, the OM Engineering Process (OMEP), to improve their runtime adaptivity and resilience. OMEP has been applied to a mobile robot in the form of a metacontroller built on top of the robot's control architecture. It exploits a functional model of the robot (TOMASys Model) to reconfigure its control if required by the situation at runtime. The functional model is based on a metamodel about controller function and structure using concepts form the ontology. The metacontroller was developed using the ontology-driven methodology and a robot control reference architecture.

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The motivation of this work is the constant evolution in the industry. Nowadays we are in what is called the fourth industrial revolution. This revolution is being fostered in many countries to get a more competitive industry. Industry 4.0 target is to make more efficient and flexible plants, reduce times and costs of projects and products lifecycle. Under this framework models appear as a core component in every new development. Using a systems engineering methodology the developed model will be the one that guarantees the consistency and derives the different applications needed in every stage of the lifecycle, from simulation, to risk assessment or even documentation maintenance. The objective of our work is to develop a model of a process plant using SysML. This model will follow a systems engineering approach, starting from the requirements and will cover the whole lifecycle of the Project. In this paper we present the development of the SysML model of a process plant (the production of ehtylbenzene from benzene and ethylene). The model includes the requirements as well as the structure, behaviour and activity diagrams. In this work an automatic transformation from the SysML model to a process simulation language (in this case Aspen Plus) has been built. This allows for the analysis of the process in the design and developement phases. The results of the simulation are fed back to the SysML model and this information is stored for further uses.

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There is an increasing demand for more autonomous systems. Enhancing systems with self-aware and self- adaptation capabilities can provide a solution to meet resilience needs. This article proposes a general design solution to build autonomous systems capable of run-time reconfiguration. The solution leverages Model-Driven Engineering with Model-Based Cognitive Control. The key idea is the integration of a metacontroller in the control architecture of the autonomous system, capable of perceiving the dysfunctional components of the control system and reconfiguring it, if necessary, at runtime. At the core of the metacontroller's operation lies a model of the system's functional architecture, which can be generated from the engineering modeling of the system.@en

Construction of robotic controllers has been usually done by the instantiation of specific architectural designs. The ASys design strategy described in this work addresses the synthesis of custom robot architectures by means of a requirements-driven application of universal design patterns. In this paper we present three of these patterns -the Epistemic Control Loop, the MetaControl and the Deep Model Reflection patterns- that constitute the core of a pattern language for a new class of adaptive and robust control architectures for autonomous robots. A reference architecture for self-aware autonomous systems is synthesised from these patterns and demonstrated in the control of an autonomous mobile robot. The term "autonomous" gains a deeper significance in this context of reflective, pattern based controllers.@en

An important part of human intelligence is the ability to use language. Humans learn how to use language in a society of language users, which is probably the most effective way to learn a language from the ground up. Principles that might allow an artificial agents to learn language this way are not known at present. Here we present a framework which begins to address this challenge. Our auto-catalytic, endogenous, reflective architecture (AERA) supports the creation of agents that can learn natural language by observation. We present results from two experiments where our S1 agent learns human communication by observing two humans interacting in a realtime mock television interview, using gesture and situated language. Results show that S1 can learn multimodal complex language and multimodal communicative acts, using a vocabulary of 100 words with numerous sentence formats, by observing unscripted interaction between the humans, with no grammar being provided to it a priori, and only high-level information about the format of the human interaction in the form of high-level goals of the interviewer and interviewee and a small ontology. The agent learns both the pragmatics, semantics, and syntax of complex sentences spoken by the human subjects on the topic of recycling of objects such as aluminum cans, glass bottles, plastic, and wood, as well as use of manual deictic reference and anaphora.@en

Four principal features of autonomous control systems are left both unaddressed and unaddressable by present-day engineering methodologies: (1) The ability to operate effectively in environments that are only partially known at design time; (2) A level of generality that allows a system to re-assess and re-define the fulfillment of its mission in light of unexpected constraints or other unforeseen changes in the environment; (3) The ability to operate effectively in environments of significant complexity; and (4) The ability to degrade gracefully - how it can continue striving to achieve its main goals when resources become scarce, or in light of other expected or unexpected constraining factors that impede its progress. We describe new methodological and engineering principles for addressing these shortcomings, that we have used to design 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. The work provides an architectural blueprint for constructing systems with high levels of operational autonomy in underspecified circumstances, starting from only a small amount of designer-specified code - a seed. Using value-driven dynamic priority scheduling to control the parallel execution of a vast number of lines of reasoning, the system accumulates increasingly useful models of its experience, resulting in recursive self-improvement that can be autonomously sustained after the machine leaves the lab, within the boundaries imposed by its designers. A prototype system named AERA 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.

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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.

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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.@en

This paper describes the development of an ontology for autonomous systems, as the initial stage of a research programme on autonomous systems' engineering within a model-based control approach. The ontology aims at providing a unified conceptual framework for the autonomous systems' stakeholders, from developers to software engineers. The modular ontology contains both generic and domain-specific concepts for autonomous systems description and engineering. The ontology serves as the basis in a methodology to obtain the autonomous system's conceptual models. The objective is to obtain and to use these models as main input for the autonomous system's model-based control system.@en

Phenomenological states are generally considered sources of intrinsic motivation for autonomous biological agents. In this paper we will address the issue of exploiting these states for robust goal-directed 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. This model of consciousness and understanding is based in the analysis and evaluation of phenomenological states along potential trajectories in the phase space of the agents. This implies that a possible strategy to follow in order to build autonomous but useful systems is to embed them with the particular, ad-hoc phenomenology that captures the requirements that define the system usefulness from a requirements-strict engineering viewpoint.@en

The construction of fully effective systems seems to pass through the proper exploitation of goal-centric self-evaluative capabilities that let the system teleologically self-manage. Emotions seem to provide this kind of functionality to biological systems and hence the interest in emotion for function sustainment in artificial systems performing in changing and uncertain environments; far beyond the media hullabaloo of displaying human-like emotion-laden faces in robots. This chapter provides a brief analysis of the scientific theories of emotion and presents an engineering approach for developing technology for robust autonomy by implementing functionality inspired in that of biological emotions.

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