Ana Inês Oliveira

Pedro Pereira

Javad Jassbi
email: j.jassbi@uninova.pt

Collaborative Safe Escape in Digital Transformation

Keywords: Collaborative Safe Escape, Collaborative Networks, Intelligent Buildings, Cyber-Physical Systems, Evacuation Process, Evacuee Decision Process

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Dealing with disasters is part of human life since the beginning. In history, many approaches and solutions could be found, and all efforts were simply targeting the life of the impacted people. This is a multidisciplinary subject while most of the works could be categorized into three classes, before, during and after disasters. As this is not a new subject, there is a rich tradition and deep knowledge and experience while emerging technologies and disruptive approaches could help and support the idea in a different way comparing with existing solutions. Regardless of all efforts, nobody could claim that the probability of disasters, either natural or human-provoked, is decreasing while forecasting techniques help us to manage the situations and reduce the consequences but still many people lose their lives or will be affected each year. This paper is focused on a very common problem which is safe escape in indoors situation in case of any hazard, with the aim to employ new concepts to assist the involved people in making the best decisions during the time of the escape process. The proposed concept is based on two fundamental pillars: Intelligent connected building, focusing on cyber-physical systems, and collaborative networks. Intelligent connected Building is simply the future of buildings while the building included all things such as furniture and materials could be a "Cyber Physical System" where everything is connected and could collect data, process and share the relevant information. The second key issue is Collaborative Networks which could be considered a key enabler to transit collective behaviour and information to decisive information based on technological solutions. Collaborative Safe Escape using new technologies addressed by IoT, IoP, Bigdata and many other emerging technologies is a new approach to save lives and reduce injuries caused by unpredictable events. The main objective of this paper is to introduce and discuss ae new paradigm of evacuation process based on the latest emerging technologies. This does not mean Smart Building or Connected Devices are new but trying to employ these technologies in a collaborative way and formalize the concept as an innovative operational model to be used in disasters is the goal of this paper. The paper is organized as follow: In the next section, the related trends and challenges are discussed. In section three, the new concept based on four Collaborative Networks is proposed, while section four is dealing with the information flow and decision process of the introduced model. Finally, section five wrap up the final remarks, conclusion and future works.

Trends and Challenges in Digital Safe Escape

A safe escape planning is very challenging due to the disordered and unorganized situation occurring in a crisis situation. Regrettably, decision makers often have an incomplete picture of hazards and potential escape opportunities. The situation turns out to be more complex due to the fact that people affected are sometimes left alone without any support that could be provided by a rescue team [START_REF] Goodwin | Ant Colony Optimisation for Planning Safe Escape Routes[END_REF]. Without any doubt, decision-making in crises situations needs to be made in the shortest time in order to minimize the potential risk or threat.

Considering the technological evolution and digital transformation that the various contexts of our society have been undergoing, there are certainly several areas of work and research in which new methodologies can be provided in order to support emergency teams and the traditional models of evacuation, naturally aligned with all the inherent legal and social constraints. In the scenario of a panic situation, it is difficult for affected people to determine what are the best decisions in an escape situation. This makes escape planning particularly difficult [START_REF] Li | Navigation protocols in sensor networks[END_REF], [START_REF] Li | Distributed algorithms for guiding navigation across a sensor network[END_REF]. On the other hand, this also opens the door to new challenges, that must be tackled, aiming a more efficient escape under a disaster scenario, reducing its consequences (material or human). Therefore, such an environment should include decision systems to enable the choice of the best way to escape, and simulation models to assist in the selection of the fastest and safest escape strategy. These models can use techniques based on agents and artificial intelligence, as is the case of the proposal of the intelligent system for disaster management [START_REF] Mirahadi | Smart Disaster Management System for Tall Buildings[END_REF]. In Fig. 1 is shown what Authors identified as relevant areas that when combined, perform a collaborative environment, are the key for an effective safe escape.

Several management system proposals are based on the use of intelligent Augmented Reality (AR) models in order to suggest guiding ways which allows rescue teams to quickly reach building occupants [START_REF] Park | Design and Implementation of a Smart IoT Based Building and Town Disaster Management System in Smart City Infrastructure[END_REF], for example in case of fire. To this end, a relationship of the physical virtual domain in the building is important.

Fig. 1. Relevant contributing areas.

There are some works and prototypes with the aim to perform tasks automatically after emergency situations. An example is the case of active disaster response system (ADRS) [START_REF] Lin | Active Disaster Response System for a Smart Building[END_REF], which acts automatically after the occurrence of an earthquake. Types of action of this type of system can be for example the opening of doors and windows, cutting water and gas pipes.

To deal with location issues, there are also several works and technological proposals to leverage and streamline solutions for a fast and efficient response in case of disasters [START_REF] Wenge | Implementation of a Visible Light based Indoor Localization System[END_REF]. A safe evacuation system by using roads, when a natural disaster occurred, is analyzed and highlighted in [START_REF] Ikeda | An Evacuation Route Planning for Safety Route Guidance System after Natural Disaster Using Multi-Objective Genetic Algorithm[END_REF]. If under such kind of disaster some roads cannot be used, or are blocked, evacuees cannot follow the evacuation procedures by just using default maps after disaster. A safety route guidance system, by using participatory sensing, that uses GPS data and accelerometer data from pedestrians' smartphone, was proposed in [START_REF] Ikeda | An Evacuation Route Planning for Safety Route Guidance System after Natural Disaster Using Multi-Objective Genetic Algorithm[END_REF]. However, the system relies on information that cannot be guaranteed, as is the case of evacuees' smartphone. On the other hand, for indoor issues, there are studies [START_REF] Kwak | An Energy-efficient and Lightweight Indoor Localization System for Internet-of-Things (IoT) Environments[END_REF] that point out that it is important to know the internal structures and the magnetic fields that buildings can generate, to be able to recognize sources of location. Related to issues of indoor location, there are numerous works that mention vast applications of fingerprint techniques [START_REF] Wenge | Implementation of a Visible Light based Indoor Localization System[END_REF], [START_REF] Luo | Secure Indoor Localization Based on Extracting Trusted Fingerprint[END_REF], [START_REF] Félix | A fingerprinting indoor localization algorithm based deep learning[END_REF]. Moreover, in the context of indoor spaces, WiFi connections are so widespread that internal location based on WiFi has attracted many research efforts. Since fingerprint techniques are considered simple and highly compatible with the hardware that exists in the interior contexts of buildings, the intersection is natural. However, due to unexpected environmental changes, the existing fingerprint location algorithms may not work as expected. There is research suggesting that it is possible, through offline mechanisms and voting systems, to improve the accuracy of the location, when effectively compared with the existing fingerprint location algorithms [START_REF] Luo | Secure Indoor Localization Based on Extracting Trusted Fingerprint[END_REF]. Other works suggests algorithms that improve received signal strength indication (RSSI) [START_REF] Xue | Improved Wi-Fi RSSI Measurement for Indoor Localization[END_REF][13], or use Bluetooth Low Energy positioning methods based on the fingerprint technique, according to Wi-Fi location techniques [START_REF] Longo | Localization and monitoring system based on BLE fingerprint method[END_REF]. These localization mechanisms are used in various contexts, such as location (non-evasive) of the elderly [START_REF] Longo | Localization and monitoring system based on BLE fingerprint method[END_REF], nonetheless, they can be used or adapted for use in emergency situations or in disruptive environments.

In [START_REF] Chou | Optimal path planning in real time for dynamic building fire rescue operations using wireless sensors and visual guidance[END_REF] is reported a firefighting scenario, affirming that most fire departments make judgments during fire rescue operations based on previous acquired knowledge, without a path-planning system that is capable of real-time information. An evacuation architecture supported by multi-agents that collects information from evacuees and from sensors in a real-time basis, aims to provide the safest and fastest escape route for different groups inside the same building, is propose in [START_REF] Khalid | A Group Evacuation Method for Smart Buildings[END_REF].

Several works on Crowd Behaviour Analysis Technology for Disaster Management [START_REF]Integrating Physical and Cyber Security for Safer Cities (A Frost & Sullivan White Paper)[END_REF][START_REF]Taking of with Digital Identity -How a single ID will transform air travel[END_REF][START_REF]Securing Experiences via Digital Identity[END_REF][START_REF]Ensuring Digital Safety with Robust Cyber Security[END_REF] have been performed and are useful in this type of situations where an inherent characteristic is the behavioural analysis of the occupants of spaces to be evacuated. In this type of situations, however, there should be a collaborative support so that the various entities related to the situation that is occurring can intervene. As so, with a support that makes these entities more prepared to collaborate in cases of incidents, their intervention can become faster and more effective. There are studies to create working models for complex emergency situations, with support in multi-risk analysis [START_REF] Bañuls | Collaborative scenario modeling in emergency management through cross-impact[END_REF] to explore different courses of action, using a Cross Impact Analysis and Interpretative Structural Modeling (CIA-ISM) [START_REF] Turoff | Collaborative Evolution of a Dynamic Scenario Model for the Interaction of Critical Infrastructures[END_REF][START_REF] Turoff | A collaborative dynamic scenario model for the interaction of critical infrastructures[END_REF] methodology to support group collaboration. In order to select collaboration partners and tasks during the formation of the collaborative crisis response network, it is necessary that various requirements are considered and parameterized [START_REF] Bodinab | Formation and performance of collaborative disaster management networks: Evidence from a Swedish wildfire response[END_REF]. There are projects, such as the SoKNOS project [START_REF] Flentge | Designing Context-Aware HCI for Collaborative Emergency Management[END_REF] to develop and test required concepts to support government, private and non-profit organizations involved in public security issues that include Human-Computer Interaction (HCI). It can also be considered systems that can help people, especially those with sensory impairments (visual or hearing) [START_REF] Baudoin | Information, communication and localization environment for travelers with sensory disabilities in public transports[END_REF]. Empowering such a Safe Escape system requires a cyber-physical infrastructure that combines new software platforms with very challenging requirements related with safety, security and privacy. Additionally, the system must be able to collect, manage and process a massive amount of data. Today's standard is the use of a cloud platform in which most data is stored, and several real-time applications are continuously running, although issues regarding data security and data protection are still quite demanding. At this point it is worth to highlight that from the Authors point of view a Safe Escape relies in a "system" that comprises different stages: data collection, processing, decision-making and actions; as depicted in Fig. 2.

A proper understanding of both "cyber" and "physical" components that considers the importance of privacy, security and safety, allied with the diverse nature of data collection, and consequently the actuation procedures that should occur, is required. One of the main challenges that must be tackled is the integration of a widely sort of distributed devices into a common framework. For instance, the different type of sensors, such as cameras, temperature, smoke, crack detection, among many others, that could be installed in a certain building, must be able to communicate/interact with each other, as well as with building occupants through smartphones, tablets, laptops and, of course, servers. From Authors' perspective, several reported works are too focused in proposing a solution at a physical layer level, sometimes forgetting the social and collaborative behaviour of evacuees. Thus, it is our understanding that a safe escape should consider: 1) aggregate data from different sources (sensors, historical data, among others), 2) new but also mature technologies in order to increase the system resilience, 3) interaction between different players, as networks, for instance building managers, and local teams, rescue teams including firefighters, police, etc., creating a collaborative ecosystem, and 4) cooperation / collaboration between cyber physical systems and smart social environment, and dynamic decisionmaking platforms.

Towards a Conceptual Model for Collaborative Escape

In order to achieve an environment to become more effective for all parties involved in the escape process, a solution based on the collaboration of all stakeholders and making use of an appropriated infrastructure is proposed. In this direction, it is aimed to achieve an environment that creates disaster awareness and support in situations of need, based on the combination of cyber-physical systems and collaborative networks paradigms (Fig. 3). On one hand, the Collaborative Networks [START_REF] Camarinha-Matos | Collaborative networks: a new scientific discipline[END_REF] paradigm can provide appropriate mechanisms, adding value to the logical collaboration between rescue entities, but also between all other parties involved, including the local spaces from where the escape needs to take place, to the users of the spaces themselves. On the other hand, the cyber-physical systems can deal with the integration of computation, networking and physical processes.

In this context, it is possible to foresee distinct types of network, namely: rescue network, local network, evacuees network, and a network of cyber-physical systems existing in the corresponding spaces. A brief description of these networks is included in Table 1. Fig. 4 shows the concept of the proposed model and the flow of information among the four networks in Safe Escape. There is an evident combination of characteristics among these networks, starting from their duration, that can be long or short term, to their membership, that can include or not members from other networks. Therefore, to have a clearer understanding of these networks, tables below include their characterization using the ARCON Framework [START_REF] Camarinha-Matos | ARCON reference models for collaborative networks[END_REF]. Being the focus, a general representation of concepts and related relationships, the ARCON's Model Intent general representation layer [START_REF]Collaborative networks: Reference modeling[END_REF] is used, converging on the description of the main elements of the endogenous and exogenous perspectives (namely on the structural and componential endogenous perspective and support and societal exogenous perspective). This characterization considers the operation stage of the lifecycle of the identified networks. The other stages, namely formation, dissolution and evolution are out of scope of the work presented in this paper and will be characterized in further works.

Roles

Planner is responsible for the formation of the evacuees network, typically some member of local network; the Coordinator is responsible for the coordination of the evacuees network during its duration; the Partner represents all the involved actors within the collaborative space of evacuees. Relations Control Supervision under the responsibility of the Coordinator; Collaboration, Exchanging and Sharing between all partners; The continuous trusting among members is vital.

Network

Temporary virtual organization involved in the process and involving the occupants of the corresponding indoor contexts. The created consortium typically dissolves after the resolution of the hazard. Subspace: Componential -Individual/ tangible elements of the network ICT Resources A software platform to support the formation and the management of the evacuees network. This platform is typically part of the platform used by the local network. Human Resources Different partners can be assigned to specific tasks of the evacuees network; Specific individuals can contribute.

Knowledge Resources

Local network Shared Resources; Templates with models or reference documents to be instantiated for a specific use case; Main common ontologies used to facilitate the common understanding among the network partners; etc. Table 5. Characterization of cyber-physical systems network.

cyber-physical systems network

Endogenous Elements Subspace: Structural -Structure and composition of the network constituting elements, as well as the roles performed by those actors

Roles

Due to the different nature of this network, namely its partners, the defined roles and corresponding relations have to be defined and configured according to the nature of the corresponding environment context. Relations

Network

Network of intelligent physical and software entities that are present in the local places. Subspace: Componential -Individual/ tangible elements of the network ICT Resources A software platform to support the formation and the management all the devices that can be part of the cyber-physical systems network. This platform is typically part of the platform used by the local network or by the rescue network. Human Resources

N/A Knowledge Resources

Local network Shared Resources; relational and no relational databases, etc.

Needless to say that the core communication channel in Collaborative Safe Network is based on technology and there is always the risk of collapsing infrastructure which causes all centralized systems to stop working. That's why it is important to make sure the system is being developed based on a modular approach and with peer to peer strategy. This can help in case of degradation and collapse of technological infrastructure to continue the support of the evacuees. Most people could use their smartphones and if it could not be connected to any central network, they could connect to each other and exchange information to support the decisions for a safe escape. Also, using temporary technological solutions such as mobile autonomous robots that could help to bring back temporary network or connect to smartphones, analyze the information and exchange messages as alternative solutions. Moreover, it is important to have different protocols and strategies to make sure collaborative networks could continue working on different occasions.

Dynamic Decision Model in Collaborative Escape

The main idea of the proposed model in this paper is based on the simple fact that the most important issue in the time of disasters is to make the right decisions at the right time. This means both efficiency and effectiveness are important and time is crucial. Not only because each second could save a life but due to the dynamism of the problem and rapid changes which could happen. For example, if the static maps show the exit path and even it was the best solution in the initial stage of the hazard, due to many factors such as the effect of infrastructure or behavior of the people, it could change from time to time. Collaborative Escape is a solution to keep the evacuees updated online and help them for the best decisions. Fig. 5 and Fig. 6 compare the decision making process of evacuees in the current situation and in the ideal situation which we call "Collaborative Safe Escape". As it could be seen, in the classic model, in the best scenario, the potential victim has the regular information, know the process and could follow the pre-existing signs and guidelines while the field observation is the only way to receive feedback. This means s/he could change his/her strategy only when could feel something is wrong which could be sometimes too late. In the proposing model, not only the smart Building as a CPS, but the cumulative behaviour of the evacuee and the information from the local network could help the rescue team and the evacuee to manage the situation. All information, considering different types of data sources, would be analyzed and different scenarios would be simulated to give the best and last guidelines to the people who are trying to escape from danger. As illustrated in Fig. 6, a variety of information and data sources and the technologies could help the evacuees to make a better decision and avoid common pitfalls such as following wrong dominant behaviour or failing by running to trap. There is no doubt that creating a Safe Escape Collaborative Network is not just technology, but a conceptual model based on CNs paradigm to employ emerging technologies in an efficient and effective way. The goal is to develop a system which could be active in case of any disaster. Smart Building as a CPSs and AI to analyze and simulate the crowd behaviour and Big data to enhance the level of services which could be provided in evacuation process in disasters to save more lives and reduce the negative impact of hazards. Fig. 6. Decision making process of evacuee in Safe Escape.

Final Remarks

Dealing with disasters is part of human life, nonetheless, several approaches and solutions have been proposed to minimize its impacts on citizens daily life. As shown, this is a multidisciplinary topic, thus can only be handled by multidisciplinary teams. In this work, Authors tried to show that smart buildings, which includes not only all things (equipment or materials) inside it, but also people, can be seen as a CPS, where everything is connected. The information management system should be able to collect data from different sources, process it and share significant information. Additionally, in a higher layer, a collaborative network should be considered as a key enabler in order to translate collective behaviour and information into decisive information thanks to technological solutions. Collaborative Safe Escape using new technologies is a new approach, that must be tackled, aiming a more efficient escape, reducing its consequences (material or human) due to unpredictable events. The characterization of the different networks presented in section 3, shall be further detailed including the specification of the functional and behavioural, and market and constituency dimensions of the endogenous and exogenous perspectives, respectively, in the Model Intent general representation layer of the ARCON reference model. Furthermore, in this work only the operation stage of the networks is characterized, thus the complete lifecycle shall be characterized in latter works. Finally, in section 4, the proposed model is discussed from the perspective of the decision-making process while the evacuee, as a potential victim, is considered as a decision maker. The combination of data sources from the networks and technologies could support the decision process and in a smart online platform, the evacuee could receive supportive information to find the best personalized solution to escape from the danger. Being this work still ongoing, it is expected that its content can provide decision-makers, as well as building owners, with supportive guidelines and directions for achieving effective and efficient safe escape.

In conclusion, this paper proposes a conceptual model which could be a revolutionary strategy to change the evacuation process in disasters. However, further research is required in areas which can be considered as interesting directions, such as: challenge of communication between the evacuees and the collaborative networks as peer to peer system; distributed computing system to keep the system running in case of failure of central infrastructure; modeling the processes and procedure in CNs and between CNs to understand the needs and to improve the efficiency according to the goals in the evacuation process; simulation of the evacuation process using the proposed model to see the weaknesses, disadvantages in practice and try to find solutions to improve it; blockchain technology to improve communications security in order to improve cybersecurity, avoiding cyberattacks aiming to create panic due to false disaster alert; Artificial Intelligence and Machine Learning can be used to improve facial recognition algorithms, which can be used to identify evacuees; etc.

Fig. 2 .

 2 Fig. 2. Structure diagram for a Safe Escape.

Fig. 3 .

 3 Fig. 3. Disaster awareness and support environment bases.

Fig. 4 .

 4 Fig. 4. Information flow in Safe Escape.

Fig. 5 .

 5 Fig. 5. Simple decision-making process of evacuee.

Table 1 .

 1 Description of involved collaborative networks. strategic network to provide support to local places or spaces in case of disaster. Places can differ in their nature, from a shopping center, to a hotel or any other indoor environment. This network includes the owner of the space, the managers, employees, etc. evacuees network Temporary virtual organization involved in the rescue process and involving the occupants of the corresponding indoor contexts. The created consortium typically dissolves after the resolution of the hazard. cyber-physical systems network Network of intelligent physical and software entities that are present in the local places.

	Network	Description
	rescue network	Long-term alliance that involves different types of rescue entities, that
		must collaborate in a disaster situation.
	local network	Long-term

Table 2 .

 2 Characterization of rescue network. Structure and composition of the network constituting elements, as well as the roles performed by those actors

			rescue network
	Endogenous Elements
	Subspace: Structural -Network Long-term alliance that involves different types of rescue entities, that must
		collaborate in a disaster situation.
	Subspace: Componential -Individual/ tangible elements of the network
	ICT	A software platform to support the rescue network management and collaboration
	Resources	among the network members.
	Human	Individuals within an organization/entity member of the rescue network.
	Resources
	Knowledge	Detailed profile of members; information about potential customers; network
	Resources	profile data; main common ontologies for common understanding; shared resources
		repository; value system; governance principles; etc.
	Exogenous Interactions
	Subspace: Support -Issues related to support services provided by third party institutions
	Network Identity	Social Nature with non-for-Profit oriented organization.
	Interaction parties	insurance entities; certification entities, etc.
	Interactions		Services Acquisition by the rescue network, such as insurance services,
			financial services, technical services, etc.; Agreements Establishment
			through protocols and actions involved in the establishment of agreements
			with third parties.
	Subspace: Societal -Issues related to interaction between the network and the society
	Network Identity	Defining the legal status of the rescue network; Defining the values and

Roles

Administrator can be performed by an entity responsible for all rescue network stage. All members can be any organization with its own competences and can actively contribute. The potential customers are among those interested in having collaboration in rescue situations.

Relations

Control Supervision is ensured by the administrator to enforce coordination and collaboration principles and agreements. All members are responsible for cooperation and collaboration, and exchange and sharing of information and resources. Also, it is essential to continuously increase the levels of trustworthiness.

Table 3 .

 3 Characterization of local network. Structure and composition of the network constituting elements, as well as the roles performed by those actorsRolesAdministrator is the responsible to administrate all phases of the local network.The Members are all the relevant participants that whose activity take place in the context of the local infrastructure. The potential customers are the clients of the local spaces.

			local network
	Endogenous Elements
	Subspace: Structural -Subspace: Componential -Individual/ tangible elements of the network
	ICT	A software platform to support the local network management and collaboration
	Resources	among the local network members.
	Human	Individuals within the entity members of the local network.
	Resources
	Knowledge	Detailed profile of members; information about potential customers; network
	Resources	profile data; main common ontologies for common understanding; shared
		resources repository; value system; governance principles; etc.
	Exogenous Interactions
	Subspace: Support -Issues related to support services provided by third party institutions
	Network Identity	Social Nature with for-Profit oriented organization.
	Interaction parties	insurance entities; certification entities, etc.
	Interactions		Services Acquisition by the local network, such as insurance services,
			financial services, technical services, etc.; Agreements Establishment
			through protocols and actions involved in the establishment of agreements
			with third parties.
	Subspace: Societal -Issues related to interaction between the network and the society
	Network Identity	Defining the legal status of the local network; Defining the values and
			principles that characterize the identity the local network.
	Interaction parties	Identifying the governmental organizations and associations that interact
			with the local network; Identifying the public or private entities
			(Regulatory Bodies) that issue regulations and standards.
	Interactions		Political Relations: defining with which parties there are political
			interactions established; Information Transfer: defining the information
			that is transferred between the local network and each third party.

Relations

Control Supervision is ensured by the administrator to enforce coordination and collaboration principles and agreements. All members are responsible for cooperation and collaboration, and exchange and sharing of information and resources. The continuous trusting among members is vital.

Network

Long-term strategic network to provide support to local places in case of disaster. Places can differ in their nature, from a shopping center, to a hotel or any other indoor environment. This network includes the owner of the space, the managers, employees, etc.

Table 4 .

 4 Characterization of evacuees network.

	Evacuees network
	Endogenous Elements
	Subspace: Structural -Structure and composition of the network constituting elements, as well as the
	roles performed by those actors

Acknowledgments. This work was funded in part by the Portuguese FCT Strategic program UID/EEA/00066/2019.


Sanaz Nikghadam-Hojjati
email: sanaznik@uninova.pt

José Barata

Computational Creativity to Design Cyber-Physical Systems in Industry 4.0

Keywords: Industry 4.0, Computational Creativity, Cyber-physical Systems, Design, Collaborative network

The Fourth industrial revolution to be successfully implemented requires higher levels of creativity and innovation in the collaboration, design and composition of its cyber-physical building blocks. Powering industry 4.0 by creativity and innovation triggers efforts for better understanding of human creativity concepts and its intersection with cyber-physical systems in industry 4.0. Since computational creativity is an emerging field of research within AI that focuses on theoretical and practical issues in the study of creativity, the goal of this paper is to motivate a discussion about the application of computational creativity to design cyber physical production systems, which are at the core of industry 4.0. The fundamental research question in the paper is about the applicability of computational creativity in an industry 4.0 context.

Introduction

The fourth industrial revolution is the information-intensive transformation of industries in an connected environment of data, people, processes, services, systems and IoT-enabled industrial assets which is looking for a way and mean to realize smart industry ecosystems of industrial innovation and collaboration [START_REF] Majstorovic | Industry 4.0 Programs Worldwide[END_REF]. The term Industry 4.0 refers to the combination of several major innovations in digital technology which includes cyber-physical systems, internet of things, cloud computing, cognitive computing and collaborative networks as its core enabler that can lead businesses to differentiated competitive advantages [START_REF] Geissbauer | A strategist's guide to Industry 4[END_REF][START_REF] Camarinha-Matos | Collaborative Networks as a Core Enabler of Industry 4.0[END_REF]. According to a survey from 1600 C-level executives in business and government across 19 countries, 87% of these business leaders believe Industry 4.0 will lead to more social and economic equality and stability [4]. Alongside collaboration as the nucleus for all dimensions of the 4th industrial revolution, creativity will be one of the top skills valued in 2020 [START_REF] Motyl | How will Change the Future Engineers' Skills in the Industry 4.0 Framework? A Questionnaire Survey[END_REF] and collaboration definitely an important aspect [START_REF] Camarinha-Matos | Collaborative Systems for Reindustrialization[END_REF][START_REF] Camarinha-Matos | Collaborative Networks of Cognitive Systems[END_REF]. According to the Zion Market Research, the global computational creativity market, which is responsible for finding creative and innovative solutions across different fields, was approximately USD 205 million in 2018 and is expected to generate around USD 1,115 million by 2026 [START_REF]Computational Creativity Market by Deployment , by Operating System , and by Application : Global Industry Perspective, Comprehensive Analysis, and Forecast[END_REF]. Collaboration has always functioned as the kernel of creative works [START_REF] Graham | Introduction: Collaborative Production in the Creative Industries[END_REF], but it seems the future of industry and digital transformation without creative collaboration and creative collaborative networks will be ambiguous.

As more and more complex and adaptive industrial systems are required, better solutions to design these new systems are needed, that should go much further ahead than just the application of fancy IT solutions such as the cloud or simple IoT approaches. Regarding these systems in a new perspective might be the only way to effectively solve the challenges imposed by industry 4.0. Seeing these systems as collaborative networks of machinery and people and applying all the theoretical framework that has been developed in the last years [START_REF] Camarinha-Matos | Collaborative Systems for Reindustrialization[END_REF][START_REF] Afsarmanesh | Collaborative Networks for a Sustainable World[END_REF][START_REF] Afsarmanesh | Collaboration in a Hyperconnected World[END_REF] can be an important contribution, together with a new vision on the importance of applying computational creativity to generate creative solutions to complex problems.

Based on mentioned aspects, the goal of this paper is to motivate a discussion about the application of computational creativity to design cyber physical production systems, which are at the core of industry 4.0.

The paper discussion is centered around the following questions:

RQ1 -Can creativity be implemented by computers? RQ2 -Is computational creativity appropriate and applicable in an industry 4.0 context? RQ3 -Is computational creativity appropriate for collaborative tasks?

Hence, this study explores industry 4.0 and computational creativity existing research works to provide better and clearer understanding of their concepts, requirements, nature and processes to answer the mentioned research questions.

Section 2 briefly outlines industry 4.0 and Cyber Physical Systems (CPS). Section 3 discusses about what creativity is and elaborates on how creativity is being introduced to the computer world (computational creativity). Section 4 describes a simple industry 4.0 system that will be used to illustrate how creativity can used to support its design. The research challenges faced by the application of computational creativity to design industry 4.0 systems composed of cyber-physical systems are listed in section 5. Finally, section 6, presents the conclusions for this paper.

Industry 4.0 and Cyber-Physical Systems

Economic challenges driven by technological and social development in the twenty first century´s world is provoking industrial enterprises to improve their agility and responsiveness in other to gain ability to manage whole value chain. Hence, enterprises require assistance of virtual and physical technologies which provide collaboration and rapid adaptation for their business and operations [START_REF] Akdil | Maturity and Readiness Model for Industry 4.0 Strategy[END_REF]. The industrial revolution of the 21 st century leads industries to be smarter in decision-making and more flexible in production volume and customization, extensive integration between customers, companies, and suppliers, and above all higher sustainability and better optimization based on environmental variables and available resources [START_REF] Shrouf | Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm[END_REF]. This movement allows much greater agility and mix in a factory without sacrificing quality, cost, or speed. That will allow the company to innovate more rapidly and gain greater revenues. In future manufacturing, enterprises must cope with the need of rapid product development, flexible production as well as complex environments [START_REF] Vyatkin | Now That's Smart![END_REF], cyber-physical systems, and collaborative networks have to enable the communication/collaboration between humans, machines and products alike [START_REF] Camarinha-Matos | Collaborative Networks as a Core Enabler of Industry 4.0[END_REF].

Cyber-Physical Systems (CPS) which has been perceived as the core foundation of industry 4.0 [START_REF] Xu | Industry 4.0: state of the art and future trends[END_REF], are systems of collaborating computation entities, that have an intensive connection with the surrounding physical systems and their ongoing processes through collaborative networks is the core foundation of Industry 4.0 [START_REF] Monostori | Cyber-physical Production Systems: Roots, Expectations and R&D Challenges[END_REF].

CPS in the 4 th industrial revolution can pursue different goals under different industrial and manufacturing circumstances. New smart CPS can drive innovation and competition in different sectors such as agriculture, energy, transportation, building design and automation, healthcare, and manufacturing. Small and medium-sized enterprises (SMEs), particularly start-up companies in the IT industry, are key participants in the development of the innovation and value creation potential of cyber physical systems.

Even though CPS strongly rely on technological advancements, the creativity, flexibility and problem-solving competence of human stakeholders is strongly needed for their operation [START_REF] Frazzon | Towards Socio-Cyber-Physical Systems in Production Networks[END_REF].

Since CPS involves transdisciplinary approaches, merging theory of cybernetics, mechatronics, collaboration, design and process science [START_REF] Lee | Introduction to Embedded Systems, A Cyber-Physical Systems Approach[END_REF], creative computation might be crucial in the design of cyber physical production systems to reduce creativity dependency from human stakeholders. This type of CPSs based on creative computation can support agile creative production to increase customer satisfaction and improve extensive integration between customers, companies, and suppliers, and above all sustainable creative economy. Hence, computational creativity-based CPS support, generate and/or evaluate creative solutions.

To achieve its basic goals industry 4.0 production systems should be basically cyber physical production systems, whose inherent collaborative and complex nature calls for highly creative designs.

Computational Creativity

The Intersection of artificial intelligence, cognitive psychology, philosophy, and the art fields is on the basis of a multidisciplinary endeavour that is known as Computational Creativity. Mechanical Creativity, Artificial Creativity and Creative Computing are other names for this fascinating new field [START_REF] Colton | Computational Creativity: Coming of Age[END_REF]. It uses computer and artificial intelligence-based technologies to study, emulate, motivate and enhance human creativity to reach one of various aims [START_REF] Colton | Computational Creativity: Coming of Age[END_REF]:

• to develop and design models, methods and computer-based programs that can stimulate and enhance human creativity without necessarily being creative themselves; • to develop and design models, methods and computer-based programs that can generate human-level creative ideas; • to better study and understand the nature and processes of human creativity and apply a computer perspective about the human creative behaviour. In summary, Computational Creativity is the capacity of finding, creating and developing solutions that are novel, interesting and appropriate for computational technologies. These aspects should go beyond human-level intellectual and computational capacity, which is far above the current state of the art in Artificial Intelligence, especially in what respects the high level cognitive functions [START_REF] Duch | Computational creativity[END_REF][START_REF] Schmid | Making AI systems more creative: the IPC-model[END_REF]. One important issue is clearly identifying the two different approaches for computational creativity: 1) using computers to stimulate human creativity, and 2) using computers to generate creative works. In this paper we are focused on the second approach.

Computational creativity cannot be achieved without a clear understanding of human creativity. Therefore, it is needed to investigate the most important cognitive models of creativity developed by psychological and behavioural scientists: 1) Psychodynamic Models, 2) Personality Models, 3) Psychometric Models, 4) Problem-Solving Models, and 4) Constraints Model of Creativity.

Psychodynamic Models of Creativity: Psychodynamic Models consider creativity an unconscious process. The unconscious mind allows creativity because it is less rigid and less specialized than the conscious mind [START_REF] Garlick | Integrating Brain Science Research With Intelligence Research[END_REF]. Some researchers find these models of creativity unsettling because they give very little credit to individual or selfcreativity.

Personality Models of Creativity: Personality Models of Creativity recognised the role played by individuals during the creative process. Garlick states that differences in an individual's ability to process information depend on brain differences [START_REF] Garlick | Integrating Brain Science Research With Intelligence Research[END_REF]. Based on this approach people with higher level of Neural plasticity can have higher level of creativity. Some find these models of creativity oppressive because they give little credit to the thought base creativity.

Psychometric Models of Creativity: Psychometric Models consider creativity as something that can be taught [START_REF] Plucker | Psychometric approaches to the study of human creativity[END_REF]. Divergent thinking and Free Association thinking empower creativity and creative idea generation that brainstorming, followed by convergent thinking transform into valuable and appropriate solutions for the problem. For Psychometric Models creativity involves three major phases: 1) Problem Consideration; 2) Thinking of possible solutions; 3) Testing or evaluation those solutions to determine whether they are useful or not [START_REF] Weisberg | Creativity: Genius and other myths[END_REF].

Problem-Solving Models of Creativity: In these models, creativity is about finding new and original solutions for problems. All searches for these novel solutions take place in a problem space. The problem space consists of the: 1) Initial state; 2) Search space and 3) Goal state. According to Weisberg, creative problem solving is a gradual development from initial knowledge to a final goal state [START_REF] Weisberg | Creativity: Genius and other myths[END_REF].

The Constraints Models of Creativity: Constraints models of creativity consider creativity as an activity in creative problem solving [START_REF] Stokes | Creativity From Constraints: the Psychology of Breakthrough[END_REF]. Walter R. Reitman argues that incremental problem-solving technique is a matter of constraints. Externally imposed and self-imposed constraints help the individual to reach a creative solution by narrowing the search space and guiding him or her towards the goal state [START_REF] Reitman | Creative Problem Solving: Notes from the Autobiography of a Fugue[END_REF].

These models can be used to understand better how the cognitive process of creativity is developed. All this work can be used to support the development of computer models that can be the basis for the development of creating creative computer programs. Computational creativity researchers from the AI area have already developed important work in creating computer programs that produce creative work [START_REF] Besold | Computational Creativity Research: Towards Creative Machines[END_REF], although not to generate industry 4.0 creative designs.

Psychologists and artificial intelligence (AI) scientists such as Margaret A. [START_REF] Boden | The creative mind: Myths and mechanisms[END_REF], believe that there are three different processes to generate creative ideas: combination, exploration, or transformation [START_REF] Boden | The creative mind: Myths and mechanisms[END_REF]. These processes, if properly applied can be the genesis for novel and valuable ideas. They may be differentiated by the sort of psychological and cognitive processes that are involved in generating the creative and useful ideas in the human brain [START_REF] Boden | Computer Models of Creativity[END_REF].

Combinational Creativity: involves generating unknown, surprizing and valuable combinations of known and usual ideas. Sometimes, the combinational creative outcomes can be unexpectedly novel, and extremely appropriate. In short, startlingly creative. Combinational creativity relies on a shared conceptual foundation and it happens by creating recognizable associations between ideas that were formerly only indirectly linked together. Combinational creativity outcomes require a fraught and valuable store of knowledge in the person´s mind, and numerous different paths to move between this knowledge. It is combinational creativity that is usually mentioned in the definitions of "creativity" and is studied by experimental psychologists and neuroscientists specialized in creativity [START_REF] Boden | Computer Models of Creativity[END_REF]. Combinational creativity is the most difficult for AI to model. Computers do not have any problem in making new and novel combinations of familiar concepts that are already stored or even accessing them. But there are two important problems. The first one is that the process of combining familiar concepts can continue for ever. The second problem is how to make these new combinations valuable and significant, which requires deep world knowledge, such as cultural knowledge. Human brain has a treasure trove of world knowledge which includes cultural knowledge. This is the missing element in generating many novel and valuable combinations by computers. However, artificial intelligence has this ability to generate novel and significant combination within a stoutly constrained context. But currently AI systems, including CPS, has no access to a rich and tightly structured storage of concepts that normal adult human has made in his/her life [START_REF] Boden | Computer Models of Creativity[END_REF].

Exploratory Creativity: it rests on using the existing stylistic rules or conventions to generate novel structures (ideas), whose possibility may or may not have been realized before the exploration took place. Usually, these rules are largely, or even wholly, implicit. Style-defining rules should not be confused with the associative rules that underlie combinational creativity. Style-defining rules are normally called "generative rules" by AI scientists. Every structure produced by following them will fit the style concerned. Most artists and scientists spend their working time engaged in exploratory creativity. It can produce higher valued structures, or ideas. This type of creativity can often offer surprises that are rather deeper than merely seeing the previously unseen. The premise of exploratory creativity is that the new idea is not in your head yet, but that ideas already in your head would only lead you to explore beyond them [START_REF] Boden | The creative mind: Myths and mechanisms[END_REF][START_REF] Boden | Computer Models of Creativity[END_REF]. AI can model the exploratory creativity by enough clarifying the rule of relevant thinking style for putting them into an artificial intelligence program. Experts in the different styles spending their lifetimes to immersion into one style and give a verbal description for them which is not suit for computer implementation. Nevertheless, modelling exploratory creativity is much more possible for AI experts then earlier processes [START_REF] Boden | Computer Models of Creativity[END_REF]. In many exploratory models, the computer comes up with results that can be compared with even professional human results [START_REF] Cohen | A Million Millennial Medicis[END_REF].

Transformational Creativity: Boden defined transformational creativity ideas as "impossibilist surprise". In transformational creativity, the space or style itself is transformed by altering (or dropping) one or more of its dimensions. As a result, ideas can now be generated that simply could not have been generated before the change.

The resulting change is so marked that the new idea may be difficult to accept, or even to understand. Sometimes, many years will have to pass before it can be valued by anyone outside a small group of aficionados. Boden believes that transformational creativity generates the most radical ideas, the ones that have the likelihood of winning a Nobel prize. The premise of transformational creativity is assuming (or hypothesizing) that an idea (or a few) in your head are actually wrong, and exploring the possibilities that result from accepting it [START_REF] Boden | Computer Models of Creativity[END_REF]. Some people believe previous processes of creativity can at least be simulated by computer, but no computer could ever achieve transformational creativity because this processes not just producing new and significant idea, it also involves in producing new way of thinking based on new generated rules. They believe computer performance is based on their specified and determined rules and cannot go beyond these rules. [START_REF] Boden | Mind as Machine: A History of Cognitive Science[END_REF] criticizes this issue and argues that what is ignored by other researcher in this matter is that the program may include rules for changing itself. She suggests programs that contain evolutionary algorithms such as genetic algorithms can make random changes in the programs own task-oriented rules [START_REF] Boden | Mind as Machine: A History of Cognitive Science[END_REF]. Insofar as computer´s performance is caused by its program, everything it does was somehow implicit in the instructions provided by its programmer. As soon as computer programs are affected by unforeseen internal or external events than genuinely new types of results can emerge [START_REF] Boden | Creativity as a Neuroscientific Mystery[END_REF].

A Production System Illustrative Example

To answer RQ2, a simple assembly production system is described to illustrate how computational creativity can used to support its design. This example was previously used to demonstrate a different concept [START_REF] Frei | Designing Self-Organization for Evolvable Assembly Systems[END_REF], but it is still very valid for the current case. The assembly of an adhesive tape roller dispenser (see Figure 3) is the task to be considered. This product consists of the Part 1 and Part 3 joined together by a screw (Part 4), and Part 2 (tape roll). The assembly operation is going to take place on top of the work-piece carrier that are transported by the conveyors [START_REF] Frei | Designing Self-Organization for Evolvable Assembly Systems[END_REF].

The choice of available system modules, for simplicity reasons, is limited to a small set (examples in Figure 2) such as: a vertical and horizontal moving axis (Z and X), and a bulk feeder for screws. What is being considering here is the different digital twin modules following the RAMI 4.0 that may be available from different suppliers, and which can be considered as candidates for creating different possible layouts to answer a customer problem or order. Figure 4 illustrates how the two robotic axis (Z and X) are combined together with a 2-finger gripper to make a two axis pick and place robot. This created pick and place unit is used to execute all the required assembly operations for the Tape Roller Dispenser [START_REF] Frei | Designing Self-Organization for Evolvable Assembly Systems[END_REF]. Layout. The computational creativity-based design tool can generate many new and significant layouts and choose the one that best answers the end-user defined constraints and available modules. The layout shown on Figure 5 is produced, which is characterized by having all robots with identical topology, as indicated in Figure 4. The loop shown in the layout of Figure 5 make the system more flexible and adaptable. Another interesting characteristic is the fact that work piece carriers are allowed to execute two differentiated tasks, any time they are visiting robot 1 (R1). In this especial example, Robot R1 works faster than Robots R2 and R4, and thus assembles Part 1 as well as Part 3. Empty work-piece carriers are input in the indicated 'IN' point, while 'OUT' represents the point where the finished product and its assigned carrier leaves the system. The letters A, B, C and D on Figure 5 are the feeders for all Parts. Part 4, the screw, can be fed by any feeder type, for instance a bowl feeder as indicated in Figure 2 that can be picked up by a magnetic screwdriver. The tape roll might be fed using a simple tubular gravitic feeder, and the main body parts can be store on pallets [START_REF] Frei | Designing Self-Organization for Evolvable Assembly Systems[END_REF].

Computational Creativity Research Challenges in Designing Cyber Physical Production Systems

The problem described in the previous section is a typical manufacturing design problem which represents one type of industry 4.0 problem that could benefit from the application of computational creativity. In this type of problems, the goal is to autonomously and intelligently generate the best layout and configuration that answers the requirements by the application of computational creativity. Hence, computational creativity-based design tools can be a new generation of tools adequate to develop Cyber-Physical based Industry 4.0 systems in which collaboration between components are an important issue. Despite the application of computational creativity being not ended on design tools, this paper is focused on this class of problems as they are enough to illustrate its basic goals. To motivate discussion and point out research directions in developing these new generation of computational creativity-based design tools (CCBDT) a sketch of a possible building blocks for such a tool is described in Figure 6.

This proposed building blocks main goal is to support the development of a new generation of design tools whose main decision process is based on computational creativity, and therefore support the development and implementation of industry 4.0 based cyber physical systems. Novel and valuable creative layouts can then be generated by machines.

The most important components or parts of this building blocks are:

1. The digital twins of the manufacturing components that can be candidates for the possible generated layouts. Essentially, the most important characteristic to be considered here is the description of the operating parameters, and their functionality or basic skills that are relevant for the considered problem; 2. The objectives that are the products to be produced (for instance, the adhesive tape roller dispenser); 3. The basic operational modules, such as the Collaborative network module, User interface, Knowledge intelligence module, and the Supervision Module that orchestrates the functionality of the tool, interacting with all the main components; 4. The knowledge management module that is responsible to manage and interact with all the different knowledge bases that are fundamental in a computational creativity process or system. As it was mentioned during the discussion of creativity and computational creativity, there is no creativity without different type of knowledge. So, technical knowledge is very relevant and fundamental, but common-sense knowledge (world knowledge) is also an important player to implement the different creative approaches (combinational, exploratory, and transformational). Ontology is included to indicate that concepts and their relations are fundamental. Of course, technical and world concepts will also be part of the ontologies. Technical and World KBs are more related to rules or set of rules that are important in the creative process. It must be noted that learning will be an important process to be included in the architecture to update the different knowledge bases; 5. Finally, the most novel part of this architecture the modules that will support the computational creative process. The idea is to have three different modules, each one responsible for one of the creative processes: combinational, exploratory, and transformational. The intention is to create a modular system that can accommodate a stepwise approach in which the system can be enriched with modules as they are being developed. If no modules to support transformational or combinational creativity are available, but exploratory modules are already available, then the system will only apply exploratory creativity. If all modules are available, then different solutions will be considered applying all the different three processes.

The proposed building blocks is much more to raise new questions than an answer to the problem. The authors' goal is much more to motivate discussion by raising some initial research challenges. Hence, the following research points might be considered as a starting point: 6).

• Understand and explore how computational creativity can be used and applied to facilitate collaboration in collaborative networks.

Conclusion

This paper is a very preliminary work but novel in terms of proposing a new approach for the application of computational creativity in the development of computer based supporting tools for the design of Cyber-physical based Industry 4.0 systems. The novelty is therefore proposing that creativity is not a magic but a type of Artificial Intelligence that can emulate the three basic types of human creativity: combinatorial, exploratory, and transformational. It is hoped that this work can motivate strong discussions and raise new research challenges based on what has been discussed and proposed on this paper. The RQ1 was positively answered in section 3, and the most important research question about the appropriateness of creativity to industry 4.0 (RQ2) is believed to be supported by what was described in sections 4 and 5. The answer to the research question 3, (RQ3) about the application of creativity in collaborative networks is also believed to be positively answered for two main reasons: 1) collaboration is an important part of the solution for industry 4.0, and 2) creative based computer tools are needed to implement and support collaborations.

A lot of work is still needed to be done in terms of implementing these three basic types of computational creativity processes, but what is important for now is that industry 4.0 can really get advantage from the application of computational creativity techniques, that have been essentially used in the areas of art, music, and literature generation. Its application in this domain, can be an important novelty and helping to bring the issue of creativity to different application areas rather than just design tools.

Another important conclusion is related to the implementation that the proposed building blocks left open. The most important challenge is how to really implement computer combinations, explorations, and transformations. A lot of work has been done in other areas (music, art, literature) that can be the starting point. Another implementational aspect is the learning that needs to be integrated in the architecture in order to keeping real time update of the knowledge bases, and all the knowledge modelling issues behind the representation of common knowledge.

The authors really look forward to having the possibility to continue proceeding this very challenging and innovative work of applying computational creativity in the world of industry 4.0 and integrate it with the collaborative networks work that has been done.

Figure 1 .

 1 Figure 1. Creativity Models Figure 1 classifies the different types of creativity models discussed in this section. The Cognitive Models of Creativity are those models developed by psychologists and behavioural scientists that explain how the creativity is processed in the brain, while AI Computer Models are those developed by computer scientists to support the implementation of creative based computer tools. So, computer scientists are developing new computer-based models that are inspired on the Cognitive Models of Creativity. Considering all that was discussed in this section the Research Question "RQ1 -Can creativity be implemented by computers?" is answered.

Figure 2 . 3 :

 23 Figure 2. Modules to be Selected Figure 3: The Adhesive Tape Roller Dispenser.From[START_REF] Frei | Designing Self-Organization for Evolvable Assembly Systems[END_REF]

Figure 4 .Figure 5 .

 45 Figure 4. Robot with one gripper and Part on a work-piece carrier. From [33]

Figure 6 .

 6 Figure 6. Possible building blocks for a CCBDT

•

  Structure the work about creativity (non-computational). It involves working with psychologists, behavior scientists, cognitive scientists, artificial intelligence scientists, and neuro scientists and really understand what has been done until now and how it can be applied to computer-based systems. The study of the existing Cognitive Models of Creativity (Figure1) needs to be understand and better studied about how they can be adapted to computer models.• Getting a good review and structure about all the computational creativity work.What Computer Models of Creativity exist, where they have been applied and how they can be used in our domain. Moreover, what new models are needed and what new field of applications in the Cyber Physical Production Systems computational creativity can be applied. • Refine and work on a more elaborated architecture for CCBDT (Figure

Acknowledgements

This work was supported in part by the FCT/MCTES (UNINOVA-CTS funding UID/EEA/00066/2019) and the FCT/MCTES project CESME -Collaborative & Evolvable Smart Manufacturing Ecosystem, funding PTDC/EEI-AUT/32410/2017.


Wojciech Cellary
email: cellary@kti.ue.poznan.pl

Non-Technical Challenges of Industry 4.0

Keywords: Cyber-physical systems, Industry 4.0, Economy 4.0, artificial intelligence, human's role, fixing errors, responsibility

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Industry 4.0 is a concept that originates from the industrial community and is currently broadly discussed by both industrial and the scientific community. Industry 4.0 emerged in the aftermath of the rapid development of information technology and robotics that together has given birth to new kinds of integrated systems [9]. There are plenty of technical issues that must be solved to make such systems run efficiently, reliably and safely [11]. There are also some non-technical issues that must be solved to prepare societies to use such systems. In this paper, in Sections 2-4 we first define Cyber-physical systems, Industry 4.0, and Economy 4.0. Then, we describe three selected challenges that have roots in technology, but that have far social consequences. In Section 5, we describe a challenge to future employees who will need to cooperate with robots controlled by artificial intelligence. In Section 6, we deal with a challenge of fixing errors in complex software used in cyber-physical systems, especially ones that are driven by big data. In Section 7, we present a challenge to determination of responsibility for errors and their consequences that without a doubt will arise when cyber-physical systems will be massively deployed. Section 8 concludes this position paper stating that each of the above challenges requires new methods and approaches that are unknown as of now. Therefore, they may be considered as directions for future research.

Cyber-Physical Systems

To understand the concept of Industry 4.0, it is necessary to start with contemporary information technology (IT). IT is currently -more than ever -focusing on data flow. The internet of people and internet of things (IoT) are mass data sources. Depending on a network usage scenario, the 5th generation of wireless telecommunications -5G -will provide high data rates across a wide coverage area, or low latency and high reliability for mission critical applications, or sporadic communication for up to one million IoT devices per square kilometer. Data storage clouds provide essentially infinite storage for the big (i.e., massive) combined data sets generated by these devices and systems. Machine learning, neural networks and other techniques of artificial intelligence permit us to efficiently process collected big data to acquire new knowledge. Feedback is provided again by the internet of people and the internet of things influencing peoples' behavior either directly or indirectly through their environments. A characteristic of contemporary IT is the fact that the above data technologies depend on one another. Only when all are applied together, do they constitute a system that provides value for people.

A consequence of the data flow system is the convergence of the physical world with the digital world forming the cyber-physical world [START_REF]Cyber-Physical Systems: Enabling a Smart and Connected World[END_REF], [START_REF]and its labour market and economic impacts[END_REF]. Up to now, the physical world coexisted with the digital world. For example, a physical bank branch coexisted with an e-banking website. Almost all the banking operations were possible in both the physical and the digital bank. Only a few operations requiring a physical signature were not available in the e-banking web. In the near future, we can imagine a bank "robot" working in the cyber-world. For example, it is possible to imagine that a customer arrives at a gas station, refuels her car, and goes out without any explicit payment operations requiring physical objects like a credit card or a mobile phone. The sum due is paid automatically by the bank robot. This is possible because the car and the face of the customer are recognized by the system of the gas company cooperating with the bank, and the sum due is automatically deduced from her account using a direct debit scheme.

Industry 4.0

One may expect that such cyber-physical systems will emerge in different areas. The most natural area is factories, where these systems are called smart factories [11], or in the broader sense -industry, called Industry 4.0 [START_REF]Industry 4.0: Definition, Design Principles, Challenges, and the Future of Employment[END_REF], [8], [9], [10], [START_REF] Schweichhart | Reference Architectural Model Industrie 4.0 (RAMI 4.0)[END_REF], [START_REF] Stich | Need and Solution to Transform the Manufacturing Industry in the Age of Industry 4.0 -A Capability Maturity Index Approach[END_REF]. A factory is a good candidate for application of cyber-physical systems, because it is a closed environment precisely controlled by factory management. Therefore, cyberphysical systems may be deployed step by step in a controlled manner, mitigating the risk involved. Industry 4.0 may be defined as the information-intensive transformation of manufacturing in a cyber-physical environment of:

 Data,  People,  Processes,  Services,  Systems, and  IoT-enabled industrial assets due to:

 Generation,  Leverage, and  Utilization of actionable information to improve innovativeness, efficiency, productivity and customization [8]. In [START_REF] Camarinha-Matos | Collaborative Networks as a Core Enabler of Industry 4.0[END_REF] collaborative networks are considered to be a core enabler of Industry 4.0 However, the above definition is not particularly revolutionary. Industry has always tried to improve its efficiency and productivity to reduce costs and increase profit, to modernize through innovations so as to get competitive advantage over competitors, and to better satisfy its customers with customization of its products. For a long time, these goals have been achieved by aligning the IT solutions responsible for generation, leverage, and utilization of information with business processes and services involving people. Thus, the revolutionary character of Industry 4.0 may be best expressed as follows:

The goal of Industry 4.0 is to provide mutually cooperating cyber-physical systems with decision-making autonomy.

It is the planned autonomy of cyber-physical systems that makes the difference between Industry 4.0 and former approaches. Former approaches required the human maker of operational decisions to take responsibility for the decision outcome. The autonomy of cyber-physical systems in Industry 4.0 is possible due to the intensive data flow mentioned above and the application of artificial intelligence techniques to process big data to extract knowledge leading to autonomous decisions.

Economy 4.0

The idea of Industry 4.0 may be extended to Economy 4.0 [6]. Economy 4.0 encompasses a full digital value chain from suppliers of materials and components required for production, through all the brokers and providers of necessary services, up to the final recipients of the entire production, i.e., end-customers, regardless of who they are:

 Entrepreneurs,  Consumers,  Owners of buildings,  Owners of retail stores,  Employees,  Citizens,  Passengers,  Patients, etc.

Economy 4.0, on the one hand, encompasses Industry 4.0 with its cooperating smart factories, while simultaneously extending smart concepts to many other sectors such as: smart energy grid, smart mobility and transportation, smart buildings, smart healthcare, smart breeding, etc. The range of Economy 4.0 may be classified as follows:

 Technologies 4.0 applied within a smart factory;  Technologies 4.0 applied for cooperation among factories (smart or not);  Manufactured smart things deployed in smart end-user environments;  Digital services provided to users of smart environments.

In Economy 4.0 logistics and supply chain management play a particular role. They may be seen as tools of integration: vertical -within a smart factory, and horizontal -between factories, and between factories and smart end-user environments [START_REF] Azevedo | Collaborative Transformation Systems -Path to Address the Challenges Around the Competitiveness of Mature Countries[END_REF].

Economy 4.0 constitutes a more challenging environment than Industry 4.0 for two main reasons. First, it is an open environment controlled by more or less vague rules obeyed or not by different independent objects. Second, people in this environment are not trained as smart factory employees about how to interact most effectively with autonomous cyber-physical systems. Both reasons increase the risk of malfunctioning, and inaccurate interpretations of the actions of cyber-physical systems.

Challenges to Future Employees

The main challenges to achieving Economy 4.0 are adding the proper level of autonomy and intelligence to systems. The goal here is not only to make the systems more intelligent, but also to make them more efficient, effective, communicating, agile and flexible, and thus well adapted to the global, inter-connected, real-time economy. A consequence of this challenge is the necessity to find a right balance between autonomous and self-organizing systems, and the planning role of humans (employees).

A future employee will have to demonstrate his/her knowledge level, and his/her ability to cooperate with machines, including robots, in real world scenarios containing fast-changing conditions, requirements, and goals. Adaptation of work methods to the requirements of Economy 4.0 requires an holistic, but agile management approach in real-time, within rapidly changing, distributed environments. This means moving from an approach based on centralized organization and planning to an approach based on ad-hoc planning and risk management.

We may then anticipate that the human's role will then be markedly transformed in Economy 4.0. The depth of the change can be illustrated by the following example showing two extremes. Currently, to provide transportation of goods, the trucks that are used are driven by truck drivers. A truck driver may be seen as an operator of systems of interconnected devices, both mechanical and electronic, that comprise the familiar vehicle called a truck. In the future, in Economy 4.0, autonomous trucks will replace human-driven trucks, so the truck driver's job will gradually disappear. A human will be required, however, to play the role of a manager of a fleet of autonomous trucks. In other words, a human will be a supervisor of autonomous systems of devices controlled by artificial intelligence. This job is much more challenging. Working in concert with artificial intelligence, an employee will need enhanced skills of thinking and reasoning to be able to supervise autonomous decisions made by the system controlled by artificial intelligence.

Challenge to Fixing Errors in Software

The ancient Romans said: "Errare humanum est" which means: "To err is human". This truth is particularly well known to programmers, who are constantly making, discovering, and fixing errors in software.

Information, being the basis of decisions may be:  Retrieved from memory,  Computed, or  Assumed. In case of erroneous data, it is possible to find the errors and correct them. In case of an erroneous program, it is possible to find wrong piece of code and correct it. But, in case of erroneous assumptions and specifications, the entire system may need to be rebuilt. Fixing errors in data or code is an operational IT activity. However, changing specifications and assumptions adopted before the system was developed is a strategic interdisciplinary activity, especially if a cyber-physical system is concerned. Redesigning the digital part of a cyber-physical system is a challenge, but re-designing the non-digital part of a cyber-physical system, e.g., mechanical one, is usually much harder. It is worth noting that specifications and assumptions may become erroneous during system exploitation due to changes in the environment the system is devoted to serve. In the case of the fast-evolving Economy 4.0, such situations may arise frequently. The problem of system reliability is even harder when we take into account the need to protect systems against cyberattacks that may be considered as a fast evolving part of (hostile) environment of cyber-physical systems.

Fixing errors in software is hard, but methods to do that have been elaborated within software engineering discipline. Fixing errors of artificial intelligence systems stemming from design specification errors or invalid assumptions will be an enormously complex challenge. If we are dealing with a neural network consisting of thousands of nodes and trained by peta-bytes of data, then finding out why it generates erroneous results may approach impossibility. Perhaps we will discover ways to allow other artificial intelligence methods to help us with that.

Challenge to Responsibility

An important aspect of human life is discovering who or what is responsible for any significant accident. Consequently, today, methods of determining who is responsible W. Cellary for an accident are well developed. In the case of Economy 4.0, however, the problems are much harder and methods for determining responsibility for errors and accidents are not yet set. Consider, for example, the above mentioned autonomous truck travelling without a human driver that is involved in a serious single vehicle accident without injuries to any person in the vicinity. There are several suspects who might be responsible for that accident. The first is the truck owner. However, the truck owner is a car rental company. If it maintained the truck properly as required by law, it would be difficult to say that it had responsibility for the accident. The second suspect is the person who rented the truck. However, the renter in this case is also a company whose warehouseman loaded some goods into the truck, carefully secured them, then entered the customer's address and clicked "go". Again, very likely this renter had nothing to do with the accident.

If the truck's mechanics were right, then the next suspect are computer systems, i.e., hardware, internet and software. In case of an autonomous truck, hardware is not only an on-board computer, but also a cloud in which programs, data, maps, etc. are stored. If the on-board computer has not been mechanically damaged, it can be checked if it worked correctly. However, the cloud cannot be easily checked. There are usually several million processors in each data center composing a cloud. Only advanced testing software may check if any of them is functioning incorrectly. It is also difficult to determine which particular processor was supporting the truck that had the accident. Moreover, each processor was allocated to serve the truck for a very short time-slot. Then, the service could be taken over by another processor, or even a different data center located in a different country.

The cause of the accident could also be a break in internet communication. The internet is a very complicated system consisting of gates, routers, cables, fiber optics, wireless communications, satellites, etc. Each data packet exchanged between the cloud and the truck can go a different way. Generally, the internet works very reliably due to error detection and repeating the transmission of incorrectly transmitted data packets, although not all packets reach their destination immediately. Occasionally, however, it fails. The impacts of such failures are often unnoticeable, but in an Industry 4.0 environment, this may change and internet failures may start resulting in actual damages.

The most suspect component for the autonomous truck accident described above is the software operating the autonomous vehicle. This does not mean, however, that assigning blame only requires us to find and charge a suspect programmer. Hundreds of programmers who do not even know each other are involved in creating such a complex software that is required to drive an autonomous truck. One team of programmers once wrote a program module solving a general problem and put it in a digital library. Another team of programmers used this module in their vehicle guidance program module. Yet another team of programmers assembled the whole software system to drive an autonomous truck from such modules developed independently. The system was thoroughly tested by independent testers. It is practically impossible to determine who is responsible for an error in a software system so large and complex that has been developed and validated by so many people.

As mentioned in Section 6, an even harder problem is finding a person responsible for errors generated by artificial intelligence. Neural networks recognize images by calculating probability based on millions of cases stored in the cloud, so the result depends not on a human, but on which cases have been recorded and used to train the neural network.

Conclusions

Current societies are more and more dependent on software systems controlling not only the digital world, but also the cyber-physical world [START_REF] Cellary | The Human-Side of Service Engineering: Advancing Technologies Impact on Service Innovation[END_REF]. With increasing deployments of autonomous cyber-physical systems, the risk of damages caused by decisions following from their malfunctioning or wrong interpretation of the situation around them increases. As we have indicated above, cyber-physical systems are controlled not only by very complex software, but also by the big data sets used to train systems of artificial intelligence. The complexity and size of programs and training data are so massive that they cannot be verified for correctness using traditional software engineering methods. On the other hand, society cannot allow the errors of such software to be dismissed as a "force majeure" (which traditionally refers to the forces of nature beyond human's control such as tornados, hurricanes, eruption of volcanos, earthquakes, tsunamis, etc.). Humankind cannot lose control over his own products. Therefore, one of the greatest challenges of modern computer science and engineering is to develop new methods of identifying the causes and fixing errors in complex information systems including artificial intelligence. The goal should not be to find and to punish a guilty programmer, but to ensure the error cannot reoccur. Industry 4.0 and Economy 4.0 will maximally challenge our educational systems. The demand for routine cognitive and physical work will decrease, because computers and robots will do such work more precisely, effectively and cheaply. However, computers and robots controlled by artificial intelligence do not understand what they manage, they only count the probability of realizing possible scenarios that have already arisen in the past, and choose the one that optimizes a given criterion, in particular, the criterion that minimizes costs of production or service provision. Thus, Industry 4.0 and Economy 4.0 will need people who think, reason and understand how to control artificial intelligence to prevent terrible mistakes that may happen if the past is mindlessly repeated. Understanding and creativity will remain the exclusive domain of humankind. Therefore, the educational system will be required to transform itself to provide Industry 4.0 and Economy 4.0 with graduates who have capabilities attuned to 4.0 systems thinking, reasoning and creativity. These are the people we will depend on to be capable to cooperate with computer systems and robots controlled by artificial intelligence in conditions of distribution, diversity and uncertainty.

Acknowledgments

. Many thanks to Louis E. Freund, Ph.D., Professor Emeritus in the Department of Industrial & Systems Engineering at San Jose State University, San Jose, CA, USA. Dr. Freund is founder and former Director of the SJSU Graduate Program in Human Factors/Ergonomics. The author is grateful for his participation in

W. Cellary many thorough discussions and for his comments that greatly improved the manuscript.