The blockchain framework has increasingly moved from purely finance and the digital world to acquire a greater role as an indispensable diaphragm between the physical and the information processing parts of Cyber-Physical Systems. A significant need is the requirement for trustworthy contact between the two domains, which today can be handled with the Oracle concept and its inherent limitations. This proposal calls on the holonic paradigm to mitigate the Oracle problem, employing a constructivistic and second-order cybernetic stance that allows the trust model of the oracle to evolve and follow the dynamics of complex reality.

Blockchain technologies and paradigms can provide new ground for the management of complex socio-technical problems like supply chains. The contribution of this work is a work-in-progress experimental setting to demonstrate the effectiveness of a novel distributed business process management scheme that integrates the RMAS (Relational-model Multi Agent System) and the Blockchain frameworks. The research work is conducted in a food supply chain case study aiming to sustainability and social good through a purposeful organisation of the autonomous behaviour of the actors in the system.

Innovative technologies and comfort-energy efficiency measures are nowadays well known and widely spread, and the main issue is to identify those that will be proven to be the more effective and reliable in the long term. With such a variety of proposed measures, the decision-maker has to compensate environmental, energy, financial and social factors to reach the best possible solution that will ensure the maximization of the comfort and energy efficiency of smart buildings satisfying at the same time the building's final user/occupant/owner needs. A work-in-progress methodological position investigates the feasibility of applying new techniques to improve the comfort and energy efficiency in smart buildings so that the maximum possible number of alternative solutions and energy efficiency measures may be considered. A simple example is used to identify the potential strengths and weaknesses of the proposed approach and highlight potential problems that may arise.

This paper presents current research on automated synthesis in the context of the Holonic Management Tree (HMT) technique. HMT has been currently challenged in robotics, manufacturing, construction, but in general this technique concerns the management of complexity that emerges in cyber-physical systems context. Although effective, the technique lacks a systematic and possibly automatic process of construction of the HMT, which impinges with artificial and extended intelligence concerns. A methodological work-in-progress position for the automation of HMT synthesis is proposed for the first time, and as a first step for an enduring but disruptive research effort.

This paper confronts with a reflection about a deep problem in computational models for cyber-physical systems (CPS). The problem arises in the contact between digital computing and the physical realm, and affects heavily the design, modeling, and implementation of CPS. Problems are exacerbated by the introduction of artificial intelligence and autonomy in industrial applications that have to meet sustainability of solutions, both in technical and societal sense. After a brief review, a new perspective and position on the future of sustainable CPS is addressed, and a pragmatic research path is presented. The RMAS (Relational-model Multi-Agent System) architecture is proposed as a test framework for the deep integration of real-world semantics into the advancements brought about by the digital transformation wave.

This paper discusses the problems of material and information flow in a complex product manufacturing process. It presents an ICT solution that augments Kanbans in order to enable flexible and pervasive control on state and delivery of material on workstations. The paper deliberates methods and tools that can be possibly applied in such production systems. The best-practices and modern technologies are taken into consideration in the context of small-medium enterprises (SMEs). It is assumed that important factors and constraints are: low costs, low instrumentations, and high flexibility. The complexity of products and the manufacturing systems makes the management and control process even more difficult. The deployment of distributed intelligence and decision support systems can facilitate the compliance with industrial standards and the migration towards the automation levels required by industry 4.0 also in SMEs.

Autonomic computing initiative aimed to develop computer systems capable of self-management in order to overcome the rapidly growing complexity of computing systems management. Similar complexity affects the management and control of the systems in the industry of the future that currently relies on the advancements in cyber-physical systems frameworks. With this position paper, the RMAS architecture is checked against the major properties of autonomic systems. RMAS is proposed as a methodological and technological platform to reduce the barriers that complexity poses to further growth in intelligent automation and control.

This work extends a performance metrics method for the treatability of some classes of problems in manufacturing automation that can be represented as a system-of-systems controlled by a cyber-physical infrastructure. With the use of proper distributed and recursive computing approaches, the complexity of the control of cyber-physical systems can be attacked through a unified and human-centered simple framework that complies with the forthcoming pervasive computing challenges posed by the smart manufacturing scenarios. The aim of this work is to provide the proof of concept of an effective methodology that relies on the decomposition of a production goal into a hierarchical self-similar structure of subgoals for the steering of the system toward improved effectiveness. An implementation of the technique is proposed by means of multiactor and multidatabase paradigms. The simulation of implementation and experimental deployment on low cost embedded device is provided.

This paper gives a novel perspective about the role of the self-similarities in the modelling of system of systems with cyber-physical representation. With the adoption of virtual overlays of dynamically self-adapting holarchies, the emergent characteristics and behaviors of the information and automation systems are mapped into a recursive tree of autonomic components that can be typically realized through holonic multiagent systems. The nested computing structures that are used to achieve a productivity goal are kept simple by means of self-similarities. This simplification provides a viable methodology for the pervasive management and the automated programming of the holonic components. The technique described is suitable for a prompt adoption on a vast class of manufacturing, robotics, mechatronics, and facility management processes, whilst enabling a continuum between humans and machines. An advantage of the proposed method is its agnosticism with respect to most of the specific but heterogeneous technological means adopted in the already existent automation realizations.

Although an industrial robot represents a higher class of machine, it is a part of a production system. The robot, regardless of its own complexity, can be represented by a set of actions that leads itself towards the achievement of production goals. In the present paper the robot actions will be modeled as a production system in order to manage and monitoring its actions toward a production target. This will allow an easier integration of the robot within the production process, as required by the new paradigms for the factories of the future.

This work reviews and recaps some lessons learnt in the industrial cyber-physical systems playground to indicate a viable path towards an effective use of artificial intelligence in distributed automation. The holon concept is used as a pivotal tool towards a simplification of the design and implementation of architectures that exploit recursive patterns and self-similarities to keep the industrial systems-of-systems problem under control, whilst addressing the consistent coupling between the physical environmental and the reasoning processes. The Beer’s viable system model is used as an inspiration towards new viable holistic visions of the production system-of-systems.

The present paper proposes a conceptual framework along with a practical solution for the problem of integrating the performance indicators of production processes with the capabilities of robotic systems and machinery. This will enable technology transfer and development of automated production processes within the new vision of industry 4.0. The proposed methodology is a solution for improving the performance of a manufacturing system that integrates robotics, mechatronics and automation systems at different levels. Performance measurements and bottlenecks detection on critical production paths are performed in order to achieve a dynamic and on-line system improvement. This consists of a pervasive control of the effectiveness of the system-of-systems, by means of a well-defined sequence of specific and minimal corrective actions. The proposed technique is advisable in the complex scenario of decentralized manufacturing. The nature of the solution is context-dependent and scalable.

The complexity of cyber-physical systems (CPSs) poses new challenges in their design, model checking and maintenance. The hardware and software designers are in search, more than ever, for simple and interoperable approaches that render the complexity of CPSs a treatable matter. In this work, database language is suggested as an enabling technology and a lean technique to the purpose. An example with best available embedded database technology is conducted by means of a deployment test on tiny embedded electronics.

The present paper proposes an effective metrics for production efficiency and a bottleneck detection algorithm of recursive nature for its application on lightweight embedded systems on board of the robotics and automation components of the factory of the future. The proposed methodology is particularly suited if the fractal paradigm is applied to the factory seen as a complex system of systems but with relevant self-similarities across the several layers of components and structures from the shop-floor up to the enterprise level. A performance test has been conducted to demonstrate the viability of the technology for tiny embedded devices with the use of declarative embedded database language. Due to the high scalability of the algorithm and its simplicity, it seems suitable also for the robotic cloud paradigm, where constituent mechatronics, sensors and actuators components are provided as a service. The results provided suggests that, with the use of similar recursive and distributed form of computing, production bottlenecks or fault detection can be scaled to address the complex and pervasive cyber-physical systems problems that characterize the 4th industrial revolution strategies.

The learning capability of neural networks is equivalent to modeling physical events that occur in the real environment. Several early works have demonstrated that neural networks belonging to some classes are universal approximators of input-output deterministic functions. Recent works extend the ability of neural networks in approximating random functions using a class of networks named stochastic neural networks (SNN). In the language of system theory, the approximation of both deterministic and stochastic functions falls within the identification of nonlinear no-memory systems. However, all the results presented so far are restricted to the case of Gaussian stochastic processes (SPs) only, or to linear transformations that guarantee this property. This paper aims at investigating the ability of stochastic neural networks to approximate nonlinear input-output random transformations, thus widening the range of applicability of these networks to nonlinear systems with memory. In particular, this study shows that networks belonging to a class named non-Gaussian stochastic approximate identity neural networks (SAINNs) are capable of approximating the solutions of large classes of nonlinear random ordinary differential transformations. The effectiveness of this approach is demonstrated and discussed by some application examples.