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Artificial Intelligence (AI) has been considered a revolutionary and world-changing science, although it is still a young field and has a long way to go before it can be established as a viable theory. Every day, new knowledge is created at an unthinkable speed, and the Big Data Driven World is already upon us. AI has developed a wide range of theories and software tools that have shown remarkable success in addressing difficult and challenging societal problems. However, the field also faces many challenges and drawbacks that have led some people to view AI with skepticism. One of the main challenges facing AI is the difference between correlation and causation, which plays an important role in AI studies. Additionally, although the term Cybernetics should be a part of AI, it was ignored for many years in AI studies. To address these issues, the Cybernetic Artificial Intelligence (CAI) field has been proposed and analyzed here for the first time. The Fuzzy Cognitive Map approach is used for creating new knowledge. An algorithm for developing the CAI approach is proposed. The only hope for the survival of the planet lies in the quick development of Cybernetic Artificial Intelligence (CAI). The importance of differentiating between professional/personal ethics and eternal values is highlighted, and their importance in future AI applications is emphasized for solving challenging societal problems. Ultimately, the future of AI heavily depends on accepting certain ethical values and the acceptance of the CAI as a viable new scientific field.

The world is facing many and different, difficult, and challenging problems. Ways are explored, to make the world a safer and better place for all humans using Ethical AI and wisdom. Certain questions as to what intelligent machines can do and what cannot do are reviewed. The importance of global cultural diversity and its relation to global coherence is analyzed. Basic definitions of artificial intelligence (AI) are given. The relation of AI and ethics are provided in some details. Technical problems associated with its design and conceptualization, the cognitive revolution, brought about by the development of AI also gives rise to social and economic problems that directly impact humanity. It is vital and urgent to examine AI from a moral point of view. A new industrial revolution is proposed, the Wise Anthropocentric Revolution: INDUSTRY 6.0. We examine the relationship between wisdom and morality within the basic framework of neo-Aristotelian Virtue Ethics

Purpose: The novel Coronavirus SARS-coV-2 outbreak late in 2019 and early 2020, known today as the COVID-19 pandemic, has spread fast throughout the world. It has considerably affected the lives of all people around the globe while the number of deaths related to the pandemic keeps increasing worldwide. Being able to predict the spread of the pandemic has been very helpful to governments to decide on actions. Statistical prediction models are capable of modeling a single snapshot but have several well-known weaknesses, such as linear assumptions between pandemic variables, while they cannot confirm the actual causality between studied factors. In the present work, the authors propose a state space Advanced Fuzzy Cognitive Maps (AFCM) approach model to predict the spread of the pandemic, using dynamic cause and effect relationships between pre-defined factors. Methods: State-Space Advanced Fuzzy Cognitive Maps are proposed for modeling the spread of the pandemic, utilizing several social, policy, and healthcare factors. Statistical data from Greece, South Korea, and Germany are gathered to evaluate the performance of the proposed model. Results: The proposed methodology was able to predict the pandemic trend in the studied countries, in terms of the total number of confirmed patient cases, yielding a coefficient of determination of 0.99, 0.94, and 0.97 respectively. The Pearson’s correlation coefficient was found to be 0.99, 0.97, and 0.98 respectively. Conclusion: The results demonstrate the effectiveness and the advantages of the proposed methodology when modeling uncertain and dynamic situations, like novel pandemics.

Fuzzy Cognitive Maps method combines the advantages of Fuzzy Logic, such as their human reasoning and linguistic features, with the advantages of Neural Networks, such as their low mathematical calculation requirements, in order to model complex dynamic systems on a wide variety of applications. The system variables and their interconnections are described using a graph and a weight matrix. Application of experts’ knowledge leads towards more realistic system models. In addition, the implementation of state-space theory in combination with learning algorithms, lead to a new generation of Fuzzy Cognitive Maps, the Advanced State Fuzzy Cognitive Maps. All the above are implemented on a nearly Zero Energy Building model, using real weather data and presenting its annual energy response.

Over the course of history, mankind has perfected its industry by not only relying on technical evolution but also by reinventing it as new resources have created new technical means. TECIS Conferences have been advocating and rightfully so, that control, automation theories and practices can contribute in making the world a more stable place for everyone. Industry has benefited from qualitative advancements which have sometimes been so ingrained in a certain time period and have had such an overwhelming impact that we have dubbed them “revolutions”. Industrialization changed the economy, transportation, health and medicine and led to many inventions and firsts in history. The first four industrial revolutions as historically been developed are review from a critical point of view. The main technological development for each one is briefly discussed. Their social impacts are highlighted. The numbering of the industrial revolutions has brought new controvercies into the world. The last 10 years many difficult problems have come upon the world. The COVID-19 pandemic has created a new difficult situation for the whole planet. The TECIS vision is of a civilization in which our technologies, systems and processes are in service to all humanity. This is called "human-machine symbiosis" and is a vision of the application of control and automation technologies and systems which benefit all life. A new sustainable economic model for the meta COVID-19 period is needed. A new concept for the humanized revolution is proposed, the INDUSTRY 6.0.

The coronavirus disease known today as COVID-19, has created tremendous chaos around the world, affecting people’s lives and causing a large number of deaths. The WHO has accepted COVID-19 as a pandemic leading to a global health emergency. Global collaboration is sought in numerous quarters. Research efforts have been intensified all around the humankind. Most studies for COVID-19 are done based on statistical models which depend solely on correlation factors. The factor of causality has not been considered appropriately. The approach of Fuzzy Cognitive Maps (FCM) that is considering the causality factors is proposed, to investigate the whole spectrum of COVID-19. An FCM COVID-19 model is proposed having 10 symptoms-concepts. Early theoretical simulation studies using an FCM COVID-19 model and real data from the local hospital, have been conducted. Simulations with real patient data give excellent results. Future research directions are provided.

Creating new knowledge is a very complicated process. Understanding the human brain is one of the greatest challenges facing 21st century science. Knowledge acquisition involves complex cognitive processes: perception, communication, and reasoning; while knowledge is also said to be related to the capacity of acknowledgement in human beings. Knowledge is the driving force for all human and physical activities. Different definition of knowledge are provided. Intelligent control (IC) and Cognitive Control (CC) are the main driving forces of creating new knowledge. The concepts of control and automation are re-examined under the combined notion of intelligence and cognition.

Buildings consume a signifcant percentage of the world’s energy resources. The rapid depletion of energy resources, has imparted researchers to focus on energy conservation and wastage. The next generation of intelligent buildings is becoming a trend to cope with the needs of energy and environmental ease in buildings. This advances the intelligent control of building to fulfll the occupants’ need. Intelligent system control for sustainable buildings is dynamic and highly complex.

The last decades the decrease of the energy consumed by the building sector has become a crucial subject. This is the reason why the modelling and control of Building Energy Management Systems (BEMS) has become a very important scientifc feld. In such a system where the human factor and the uncertainties caused by its presence play a very important role the need for intelligent, cognitive modelling methodologies becomes obvious. The State Fuzzy Cognitive Maps Methodology combined with Nonlinear Hebbian Learning techniques are used to model the behaviour of a BEMS and improve its response.

The aim of this article is to present a methodology and an approach for energy efficiency optimization for buildings, connected in microgrids. The initial investment costs for the building and the energy costs are optimized while the environmental pollution is minimized at the same time. A bi-criterion optimization problem is formulated. It is solved by a multi-objective genetic algorithm in MATLAB. The possibilities of the approach are illustrated by the optimization of the energy efficiency of a group of three-storey houses connected in a microgrid. The obtained results demonstrate that the proposed approach could be implemented for different real problems concerning the buildings energy efficiency and may be helpful for construction managers, architects and decision makers in this area.

Challenging Issues of International Economic and Political Stability is considered. Various models describing them are briefly presented and their strong and weak points are considered. Today’s world problem cannot be analysed and understood with present mathematical models. The approach of Fuzzy Cognitive Maps (FCM) is used to model the economic behaviour of a country. A new model is proposed for international affairs using FCM. Two cases are considered, and simulations are conducted. Obtained results are presented and discussed. Future research directions are presented.

Customer retention and relationships with customers are the main focus of global companies who intend to stay competitive and have profit sustainability. Since companies’ main focus is higher profit, then having customer loyalty is the key to that. Loyal customers are advocates of companies with positive word-of-mouth, recommendations and future buying. This research is a country case study and its main objective is to understand the effect of switching-costs, product quality and corporate image on customer loyalty in the retail market in Kosovo. This study suggests that companies should focus on the store location, products’ price, quality and corporate image if they want to have lower costs, create competitive advantage and have higher profit. This research has been conducted by collecting primary data through questionnaires. Statistical analysis using SPSS Statistics were conducted during the research.

This paper discusses the related to the post-conflict developing regions, respectively transition regions which are a special subject of studies related to the deployment of technology in business processes. In typical circumstances Electronic Data Interchange and other digital technology become applied with the aim to improve the businesses effectiveness and efficiency by enhancing information quality, by increasing the information interchanging speed, by increasing business process flexibility and by improving operations and reliability of business network. Nevertheless, the purpose of digital technology at the operational level remains the same, the implementation and the goal are different when it comes to be implemented in specific regions, e.g. post-conflict and transition regions. While in developed economies the main objective of digital technology implementation is improvement of quality of information, in developing transition regions the implementation of EDI represents greater challenge since the main objective could be oriented towards economic stability.

The scale and depth of economic integration that has taken place the last three decades (1975-2017) has raised concerns among scholars that these developments are reminiscent of the era of pre-Great Depression liberalization. Critics of globalization point out that the removal of trade barriers and capital controls would tilt the balance of power towards capital holders and richer economies. This undermines state sovereignty, democracy and internal stability. A growing number of scholars have dismissed those concerns pointing out the net benefits of trade and globalization as well as the reduction of world poverty. This paper finds that concerns of the demise of the nation state due to increased economic globalization are exaggerated. Increased liberalization has only served to strengthen the role of governments. However, the net gains from increased economic exchange have not been equally shared domestically or across trading regions. Countries that have embraced globalization but maintained their reservations over its negative effects on their people have made most benefits and positive gains.

Human kind is faced with many and difficult problems and challenges. They need urgently solutions. The Academic and Scientific world must wisely address, find and provide realistic and sustainable solutions to these problems. Many of them have been as the result of human misuse of some powerful scientific and technological achievements. Many theories have been developed and use successfully in solving everyday problems of the society. The humankind needs a peaceful world with global coherence and stability as well as a steady and sustainable economic growth. Two very interesting scientific developments are: Intelligent Control (IC) and Cognitive Control (CC). Both scientific fields have developed excellent theories, techniques and tools but each one on its own field. No much interdisciplinary interaction between them. A new scientific field is needed to connect mathematically IC and CC. This new approach is to be referred as the Intelligent Cognitive Unified Control (ICUC) theory. If applied wisely there is a hope for a global coherence and sustainable economic growth for the humankind.

In recent years, Fuzzy Cognitive maps (FCM), although they are a relatively new theory, have been used in a variety of applications and have been improved using learning algorithms and experts’ experience. In this paper a different approach in the basic FCM equation will be presented. First of all concepts will be separated in inputs, states and outputs, and a new concept calculation equation will be proposed. In addition, Anti Windup method is replacing sigmoid function. All the above will be applied on a FCM modeling a nearly Zero Energy Building (nZEB). The method of FCMs, challenging and promising at the same time, still has place for improvement and evolution.

The world is facing many and different, difficult and challenging problems. New knowledge is the driving force for sustainable global and regional development. Science plays a central and crucial role in creating new knowledge. Education and knowledge are the basis for the “Triangle of knowledge” which has played a central role in the decision making process of many governments around the world. Many, various and different forces are shaping the new world. Using the concerted efforts of Intelligent and Cognitive systems we can give optimum solutions to the many complex problems that have emerged A new triangle of knowledge is developed based on the old triptych but now including the concept of True Knowledge acquired through Wisdom and practical Philosophy. Ways to make the world a better place for all humans using wisdom and philosophy are explored. The Intelligent and Cognitive Unified Control (ICUC) is proposed as a new method for modelling and controlling complex dynamic systems.

A new approach of Dynamic Fuzzy Cognitive Knowledge Networks is presented. This is an evolutionary type of Fuzzy Cognitive Maps (FCM) that arose from the need for updating classic methodology in order to overcome its drawbacks, concerning the single calculation rule, stability and real time problems and expand its use in a variety of applications. This new approach is being tested for its accuracy in Decision Support Systems in medicine, trying to model knee injuries by using 17 real cases of patients. The new proposed model is able to diagnose meniscus injuries and to distinguish between acute and degenerative injury. Subsequently we observe the evolution of the injury by administering a proposed treatment by the physician. Results of this new method, which are presented in detail, are very satisfactory for both two levels and treatment stage, and in total agreement with Magnetic Resonance Imaging outcomes. The whole methodology is the outcome of a close collaboration between engineers and medical doctors and is significant because it is a promising tool which sets aside the main disadvantages of Fuzzy Cognitive Maps and allows us a wide use in many real time problems.

The panel discussion involves the organisers and one author from each of four papers presented in a session on engineering ethics, emerging challenges and socio-cultural effects. The papers consider a wide range of issues of great importance to the engineering community and beyond it to the population as a whole. They include the contribution of engineering to sustainable development, the need for more women engineers, roboethics and the development of a low cost robotic arm to support independence. The discussion will involve the audience in further consideration of the issues raised in the session and enable them to both make contributions and put questions to the panel.

Learning is the most important thing that living creatures do. An organism cannot properly animate itself without first learning how to. Knowledge is the basis for all natural and human made systems. Using knowledge to model and control a complex dynamic system (CDS) is considered. Wisdom is carefully reviewed and related to knowledge. Deep Learning (DL) and Wise Learning (WL) are presented and analyzed as two approaches to address the challenging problem of modelling and controlling CDS. Strong and weak issues of both methods are discussed. Future research directions are provided.

The energy sector worldwide faces significant challenges that everyday become even more acute. Innovative technologies and energy efficiency measures are nowadays well known and widely spread, and the main issue is to identify the more effective and reliable in the long term. In order to do so the decision maker has to compensate many factors. This article investigates the feasibility of the application of new intelligent control methods in modeling and controlling the performance of a building. For the first time a performance comparison of Fuzzy Logic vs Fuzzy Cognitive Maps (FCMs) theories is performed and interesting results are presented. Multi-level intelligent controllers to manage the various parts of a building's automation and increase its energy efficiency are presented. For the first time the Building Energy Management System has been modeled using FCMs. Simulation studies with real environmental data have been performed and useful results have been obtained and discussed. Challenging future research topics are provided.

ZEBs (zero energy buildings) and more specifically “net Zero Energy Building” (nZEB) have become a prominent wording to describe the synergy of energy efficient building and renewable energy utilization to reach a balanced energy budget over a yearly cycle. The lack of a common and accepted definition or even a good understanding of ZEB makes the approach of this problem very challenging. In this paper there is an evaluation of the criteria in the definition framework and selection of the related options. Also a methodology is approached, to set nZEB definitions in a systematic way. Today's different ZEB technologies, challenges and problems are identified and analyzed. For the first time the overall operation of nZEB and its performance is investigated and modeled using FCMs and learning algorithms. A set of low energy buildings using extensive energy and environmental data are simulated for all four seasons of a year. Interesting results are obtained, presented and discussed. Useful conclusions are drown and future research challenging topics are presented.

A critical overview of modelling Business and Management (B&M) Systems using Fuzzy Cognitive Maps is presented. A limited but illustrative number of specific applications of Fuzzy Cognitive Maps in diverse B&M systems, such as e business, performance assessment, decision making, human resources management, planning and investment decision making processes is provided and briefly analyzed. The limited survey is given in a table with statics of using FCMs in B&M systems during the last 15 years. The limited survey shows that the applications of Fuzzy Cognitive Maps to today’s Business and Management studies has been steadily increased especially during the last 5-6 years. Interesting conclusions and future research directions are highlighted.

The aim of this paper is to use advanced control theories to model the total energy behavior of an autonomous building for residential or commercial use. The proposed method is using Fuzzy Cognitive Maps to model such intelligent buildings. Two Fuzzy Cognitive Maps are constructed. The energy requirements for a building from the southern part of Greece are being studied in order to construct the aforementioned controllers.

This work presents the experience gained by Patras Science Park through interregional growth projects. This experience is very useful to regional and central authorities that determine growth policies and to any actor who works towards regional growth especially through innovation and technology, such as science and technology parks (STPs). HuReDePIS interregional project examined growth potential of the areas around Ionian Sea for six strategic sectors: SMEs, services, environment, CAP, culture and tourism and there were initiated growth and an immigration network. The current situation for the six sectors is highlighted and the problems and difficulties for regional and spatial development are identified and diagnosed. Special emphasis is given to the important role of innovation for growth of a region. The key role of STPs for growth is validated by their ability to serve as mediators between knowledge producer organisations and local economic factors.

Fuzzy cognitive maps (FCMs) constitute an attractive modeling approach that encompasses advantageous features. The most pronounces are the flexibility in system design, model and control, the comprehensive operation and the abstractive representation of complex systems. The main deficiencies of FCMs are the critical dependence on the initial expert’s beliefs, the recalculation of the weights corresponding to each concept every time a new strategy is adopted and the potential convergence to undesired equilibrium states. In order to update the initial knowledge of human experts and to combine the human experts’ structural knowledge with the training from data, a learning methodology for FCMs is proposed. This learning method, based on nonlinear Hebbian-type learning algorithm, is used to adapt the cause–effect relationships of the FCM model improving the efficiency and robustness of FCMs. A process control problem is presented and its process is investigated using the proposed weight adaptation technique.

This work emphasizes on the implementation of soft computing techniques for the production planning of complex manufacturing plants. A hierarchical control structure has been assumed to control and to optimize the chemical process of a complex plant with successful results and at the highest hierarchical level the production planning should meet the fluctuating demands in optimal way. This level integrates the operational and business management requirements where the soft computing technique of Fuzzy Cognitive Maps is proposed to model these tasks. The strategic planning for the hierarchical integrated system realizes the optimal total management at the corporate level. The obtained simulation results prove the applicability of the proposed methodology and the advantages of using soft computing modeling techniques for the sophisticated high level of hierarchical systems.

Hierarchical decision support system in emergencies on expert systems is suggested. Knowledge bases of the expert systems are created as a result of simulating of a complex dynamic objects management in emergencies and developing the thesaurus of a problem area on the basis of the simulation. Methodological aspects of classification the natural - language cases descriptions based on linguistic analysis of subject area are under discussion. The structure of the thesaurus of subject area is considred. Architecture of information decision support system is suggested

This paper examines the usefulness of Fuzzy Cognitive Maps in modeling complex systems and specifically their use in modeling supervisory manufacturing systems and handling with information from an abstract point of view. The use of Fuzzy Cognitive Maps for developing behavioral model for a complex system is discussed. Fuzzy Cognitive Maps applicability in modeling complex systems is presented and a hierarchical structure is proposed to model and control a complex system. Complex system is decomposed in subsystems and for each one a FCM model is developed. A hierarchical structure is proposed where Fuzzy Cognitive Map models the supervisor of the system and develops an abstract conceptual model of the complex system.

The problem of modelling and control of complex systems is addressed. The interrelationship of systems-intelligence and control is analysed and ways to develop Intelligent Control Systems (ICS) are discussed. One useful approach, the Fuzzy Cognitive Maps (FCM), is briefly presented. Issues and challenges that the systems society is confronting with are discussed. Thus researchers and developers who are exploring ways to develop Intelligent Control Systems must give careful attention to the distinctive challenges and unique opportunities of the strong interrelationship and interdependence of the three fundamental fields of science; computers-communications-control.

There are considered the creation problems of organizational and manufacturing systems as formalized system-formation process according to the construction conception of resource-goal triads. Triad is formed and is used as the base element of description formalization of the purpose achievement procedure by means of system-formation process and connects three components necessary for creation of any system (reasons of system origin, existence purpose, possibility of creation). The proposed procedure of system-formation is a basis for development of theoretical bases for modeling and research of controlled manufacturing systems.

The paper examines the usefulness of Fuzzy Cognitive Maps in modeling complex systems and specifically their use in modeling manufacturing systems and information from an abstract point of view. Aspects such as Fuzzy Cognitive Map representation and development are presented and PCM use to develop a behavioral model of the system is discussed. Fuzzy Cognitive Maps applicability in modeling complex systems and their use to aggregate different models for the complex system is discussed. A hierarchical structure is proposed, where a Fuzzy Cognitive Map models the supervisor of the system.

A multilevel hierarchical control system for a single end product multistage plant is under consideration. It is shown that a variety of models and/or hybrid modeling are reasonable for inferred key variable forming, monitoring and control. Because of the complexity, uncompleted information, large uncertainties and mixture of batch, semi-batch and continuous processes different control strategies are appropriate for each hierarchical level. Two level reactive scheduler is proposed. Integration of operational and business management is carried out at the top hierarchical level using Fuzzy Cognitive Maps (FCM) approach. Some results of practical application of the system are reported.

The present research examines the challenge that academic institutions face in developing appropriate skills within the engineering community. First the general issues of Control education and future Manufacturing challenges are briefly reviewed and analysed. This work attempts to qualify the areas, which have to be addressed as well as to position future Control and Manufacturing requirements so that opportunities can be identified, these are associated to the results of a survey undertaken by Laboratory for Automation and Robotics on current academic programs on Control and Manufacturing engineering available in Institutions throughout Europe. Finally the research reaches to some generic conclusions regarding the standards met by the existing Control and Manufacturing curricula and proposes some recommendations for academia.

In this paper a class of two level systems where N linear subsystems S1, S2,…, SN in a lower hierarchical level are interconnected through a cordinator S0in a higher hierarchical level is considered. The model of such a system is presented together with a controlling algorithm that preserves the structure of the system. The paper is mainly a survey of all related work and presents all the critical aspects of such a controlling scheme such as structural controllability, fixed modes, the algorithm itself and stability of the closed loop system.

In Complex Large Scale Systems there is an oncoming need for more autonomous and intelligent systems, new methodologies from discipline research areas have been proposed. A general formulation for the Overall Control Problem of Complex Systems is presented. Then, the use of a hybrid methodology, which combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for the modeling Supervisory Complex Systems, using is investigated. The description and the construction of Fuzzy Cognitive Map is examined and a model for the supervisor is proposed.

The paper considers the main characteristics of sway (design&control&learning) process with new cybernetical means - hierarchical networks of States and unions of States. The hierarchical networks are defined in agreement with real world construction which connects the strata (levels, times) created by its history and new ones whose hazy images belong now to the area of thoughts. The new mechanism of mathematics (aed theory) allows to turn these images into symbols (figures) convenient in sway process. The strategy of new time constructing acquires in suggested symbols of aed theory the lines of mathematical records which have identical understanding in wide scientific and practical ranges able to change this strategy. Owing to that the high hazard of activity in hazy areas is decreased.

Virtual enterprise (VE) is a new form of independent enterprises co-operation. VE configuring requires an analysis of current enterprise facilities along with its future goals. The results of such an analysis will be required for determining an appropriate reference configuration to build an enterprise. During the configuring, structure and elements of systems must be modified and adapted to new goals. The organisation of these processes, called VE Configuration Management, constitutes the main focus of this paper. The general objectives of the paper are to determine a methodology, a set of models, and a decision support system architecture based on multi-agent and AI techniques for configuration management.

In this paper the application of Fuzzy Cognitive Map (FCM) in controlling a process problem and its use in modelling Supervisory Manufacturing Systems is presented. The description, construction and the mathematical model of Fuzzy Cognitive Map are examined. Then, a chemical process is modelled with FCM and its behaviour is simulated and the application of Fuzzy Cognitive Maps in the mode ling of the Supervisor of Manufacturing Systems is discussed.

The problem of production control as of a complex dynamic object is under discussion. There are elaborated and studied intelligent control algorithms using modelling results of system functioning dynamics which testify to uncertainty of environment. The proposed procedure of information exchange allows to unit effectively the dynamic and intelligent algorithms as well as man participation in decision making.

This paper investigates the implementation of a hybrid methodology, which combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for the modeling of the supervisor of Large Scale Systems. The description and the construction of Fuzzy Cognitive Map will be extensively examined and it will be proposed a model for the supervisor. There is an oncoming need for more autonomous and intelligent systems, especially in Large Scale Systems and the application of Fuzzy Cognitive Map for the modeling of the Supervisor may contribute in the development of more autonomous systems.

Intelligent Manufacturing Systems is the major challenge that must be met by scientific community in order to develop the next generation sophisticated manufacturing systems. This paper presents an overview of new promising domains, such as fuzzy logic, neural networks and hybrid intelligent control systems, that could be utilised to achieve human intention for more advanced manufacturing systems. The modelling of the supervisor of manufacturing systems using fuzzy cognitive map is proposed and system functionalization architecture is presented. Finally, conclusions of an international symposium on issues and challenges of Manufacturing and Control Education for the 21stCentury are presented.

The regulation of a decentralized large-scale linear system which consists of N subsystems S1, S2,…,SN interconnected with a subsystem S0 is investigated. Since in the state-space description the matrix describing the dynamics of this decentralized large-scale system consists of block elements depicting an arrow structure, the system is called a block arrow structure (BAS) decentralized large-scale system and its regulation problem is called a BAS-decentralized large-scale linear regulator problem. In this paper, the time-invariant BAS-decentralized large-scale linear regulator problem is mathematically formulated and a solution to this problem is given by developing a suboptimal linear state feedback controller with the features of (a) partial decentralization (utilizing the interconnections), (b) parallel implementation, and (c) structure flexibility so that the addition and/or deletion of any subsystems within the BAS model specifications does not require the solution of the problem from the beginning again.

This paper examines fuzzy cognitive map (FCM) theory and its use in supervisory control systems. An FCM is a graph used to depict cause and effect between concepts that stand for the states and variables of the system. An FCM represents the whole system in a symbolic manner, just as humans have stored the operation of the system in their brains, thus it is possible to help man's intention for more intelligent and autonomous systems. FCM representation, construction and a mathematical model are examined; a generic system is proposed and the implementation of FCM in a process control problem is illustrated and a model for supervisors of manufacturing systems is discussed. Although an FCM seems to be a simple model of system behaviour, it appears to be a powerful and effective tool describing the behaviour of a system and representing the accumulated knowledge of a system.