The lack of a well established theory of consciousness is a major difficulty in the construction of machines that express some of the functionalities associated to them. The ASys long term project intends the construction of assets for engineering any-scale, self-aware autonomous systems. A core element in this effort is the proposal of an architecture for consciousness of general applicability. In this article we analyze some of the current problems present in the construction of machines based on cognitive models and propose a model-based strategy to enhance the rigor of the theorization on consciousness and its mapping into realizations in technical systems.@en

Ontologies provide a common conceptualisation that can be shared by all stakeholders involved in an engineering development process. They provide a good means to analyse the knowledge domain, allowing to separate the descriptive and the problem-solving knowledge. They can also be as generic as needed allowing its reuse and easy extension. These features made ontologies useful for representing the knowledge of software engineering techniques applied to autonomous systems. This work describes an ontology-based framework consisting of two intertwined elements: a domain ontology for autonomous systems (OASys) to capture any autonomous system's structure, function, and behaviour; and an ontology-based engineering methodology that generates models for autonomous systems, based on the knowledge contained in OASys and other domain ontologies. Both elements have been used in a case study to assess the suitability of the developed framework.@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

Autonomous systems refer to systems capable of operating in a real world environment without any form of external control for extended periods of time. Autonomy is a desired goal for every system as it improves its performance, safety and profit. Ontologies are a way to conceptualize the knowledge of a specific domain. In this paper an ontology for the description of autonomous systems as well as for its development (engineering) is presented and applied to a process. This ontology is intended to be applied and used to generate final applications following a model driven methodology.@en

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.@en

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.@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

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

This paper describes a work in progress on an ontology-based approach for autonomous system engineering. This is part of the ASys research programme which focuses on the development of a technology for autonomous systems product-line engineering. Such technology will comprehend from platform technology to designs of intelligent autonomous systems and support methodologies. Within this programme, a domain-ontology for autonomous systems (OASys) has been developed to describe the autonomous system's domain. OASys will be used as core software asset for the analysis, design, implementation, and run-time operation of any autonomous system. Two testbeds are being developed to test OASys's applicability: a mobile robot-based application, and the control system of a chemical reactor.@en

In this paper we will argue that given certain conditions for the evolution of biological controllers, they will necessarily evolve in the direction of incorporating consciousness capabilities. We will also see what are the necessary mechanics for the provision of these capabilities and extrapolate this vision to the world of artificial systems.@en

Conceptual modelling aims at identifying, and characterising the entities and the relationships of a selected phenomenon in some domain. The obtained conceptual models express the meaning of the concepts used by domain experts, and the relationships between them. An ontology is a for- mal specification of a common conceptualisation shared by stakeholders and experts in a domain. Ontologies can serve as the semantic support for conceptual modelling, guiding and constraining the intended meaning of the conceptual models. We have followed this approach in our model-based control systems, by developing a domain ontology and an ontology- based methodology to support the conceptual modelling of autonomous systems. The ontology for autonomous systems captures the ontological elements to describe an autonomous system's structure, function, and behaviour. The methodology exploits the ontology to generate the conceptual models for a generic engineering process. Both elements have been used in case studies to obtain conclusions on the suitability of the developed ontology and its application in the model-based engineering of autonomous systems.@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.@en