Past, Present, and Future of Industrial Agent Applications - IEEE Xplore

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 9, NO. 4, NOVEMBER 2013

Past, Present, and Future of Industrial Agent Applications Paulo Leitão, Senior Member, IEEE, Vladimír Mařík, Member, IEEE, and Pavel Vrba, Member, IEEE

Abstract—Industrial agents technology leverages the benefits of multiagent systems, distributed computing, artificial intelligence techniques and semantics in the field of production, services and infrastructure sectors, providing a new way to design and engineer control solutions based on the decentralization of control over distributed structures. The key drivers for this application are the benefits of agent-based industrial systems, namely in terms of robustness, scalability, reconfigurability and productivity, all of which translate to a greater competitive advantage. This manuscript monitors the chronology of research and development of the industrial applications of multiagent and holonic systems. It provides the comprehensive overview of methodologies, architectures and applications of agents in industrial domain from early nineties up to present. It also gives an outlook of the current trends as well as challenges and possible future application domains of industrial agents. Index Terms—Holonic systems, industrial agents, multiagent systems (MAS), production planning, real-time control, semantic technologies manufacturing.

I. INTRODUCTION

T

HE multiagent system (MAS) approach [1], [2] constitutes a novel software engineering paradigm that offers a new alternative to design decision-making systems based on the decentralization of functions over a set of distributed entities. The MAS paradigm replaces the centralized control by a distributed functioning where the interactions among individuals lead to the emergence of “intelligent” global behavior. This allows reaching a high degree of autonomy and cooperation, without a fixed client-server structure. The concept of industrial agents is related to the application of the multiagent systems technology to solve complex industrial problems, ranging from manufacturing and logistics to military and space applications. Industrial agents provide several advantages, namely in terms of robustness, scalability, reconfigura-

Manuscript received December 15, 2011; revised April 05, 2012; accepted August 07, 2012. Date of publication October 02, 2012; date of current version October 14, 2013. This work was supported in part by the Grant Agency of the Czech Technical University in Prague under Grant SGS12/188/OHK3/3T/13, and by the Ministry of Education of the Czech Republic within the Research Program MSM6840770038: Decision Making and Control for Manufacturing III. Paper no. TII-11-1015. P. Leitão is with the Department of Electrical Engineering, Polytechnic Institute of Bragança, 5301-857 Bragança, PT, Portugal (e-mail: [email protected]). P. Vrba and V. Mařík are with the Department of Cybernetics, Czech Technical University in Prague, 166 27 Prague 6, Czech Republic (e-mail: pavel. [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TII.2012.2222034

bility and productivity, all of which translate to a greater competitive advantage. On the contrary, there are also several drawbacks discussed later in the paper that hinder from large scale exploitation of agent concepts in industry like questionable return on investment, lack of development tools and standards, lack of skilled design, engineering and maintenance personnel, and many others. This paper intends to survey the application of the agent technology to solve the industrial problems, however does not tend to cover theoretical issues of agents principles. It presents a chronology analysis over the past, present and future era, for each of which the developed methodologies and architectures, typical application domains and notable applications are listed and briefly discussed (see Fig.1). In addition, an attempt is made to identify major trends to get the complete picture about the historical evolution of industrial agent applications. The borderline between the past and present is set to year 2005 although it is clear that it has to be considered as blurred and that some of the discussed entities span across more eras. But the year 2005 can be considered as the end of the early stage of industrial applications of agent technology. The first agent-based architectures were proposed, large-scale industrial demonstrators were already in-place, the first standards, like FIPA standards or IEC 61499 [3], were drafted, and the first platforms and supporting tools were available. On the other hand, it became quite clear, that the area of agent technology deployment needs a new impulse to move towards really viable and flexible systems satisfying the industrial needs concerning interoperability, robustness, flexibility, scalability, reconfigurability, and productivity. Thus, the comparatively massive exploration of ontologies and semantic knowledge, new approaches to internal knowledge models/architectures and knowledge sharing together with new system integration paradigms and reconfiguration algorithms entered the scene. This is the situation as today. From the current trends as well from the demanding industrial needs we can extrapolate the future technology visions for the industrial agent-based solutions. The paper is organized as follows: first, Section II makes a retrospective of the use of industrial agents. Section III presents an overview of the present applications and ongoing projects and initiatives. Section IV discusses the future trends in industrial agents and, finally, Section V rounds up the paper with the conclusions. II. PAST This section makes a retrospective of the use of industrial agents, by analyzing the architectures and methods, past industrial applications and successful R&D projects.

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LEITÃO et al.: PAST, PRESENT, AND FUTURE OF INDUSTRIAL AGENT APPLICATIONS

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Fig. 1. Time axis showing evolution of agent-based architectures and applications in industrial domain.

A. Architectures and Methods Holonic manufacturing systems (HMS) was a research initiative developed as part of the international intelligent manufacturing systems (IMS) program. Its intention was to bring the holonic concepts originally developed by Arthur Koestler into the manufacturing world. From observations of both the living organisms and the social organizations Koestler derived concept of holon to describe a hybrid nature of self-contained entities that are simultaneously wholes and parts [4]. In fact, a holarchy is a hierarchy of self-regulating holons that are simultaneously autonomous wholes for their lower parts and dependent parts for higher control levels. Holons possess a degree of autonomy to handle contingencies without asking the higher authorities for instructions; yet they are subject of control from those higher levels. Such organization exhibits very stable behaviour and ability to cope with disturbances and unforeseen situations. HMS applies similar principles to manufacturing sphere. The control functionality is distributed among holons, representing manufacturing entities like factories, production lines, machines, workers, materials, orders, jobs, and the like. Holons are able to work autonomously and cooperatively, yet in the proper context of the larger organization. These considerations lead to the vision of a holonic factory [5] as philosophical metaphor which has never been fully implemented. The holonic visions in the area of industrial control resulted in development of the IEC 61499 standard based on function blocks and their communication on the lowest control and communication level. This standard represents the key results of the HMS initiative and has a great future potential as discussed further. Several holonic methodologies and architectures for manufacturing domain were proposed aiming at the formalization of types of holons, their responsibilities and behaviors, and the interaction scenarios. It is for instance the product, resource, order, staff architecture (PROSA) [6], adaptive component based architecture (ADACOR) [7], or holonic component based archi-

tecture (HCBA) [8]. There are other methodologies aimed at industrial domain such as AARIA [9], MetaMorph [10] or MASCADA [11]. The holonic factory perspective brought later the vision of virtual enterprises that represent temporarily created partnerships among independent companies in order to gain a competitive advantage by sharing skills, competencies and resources [12]. Along with standardization carried out within HMS there was an intense effort to deliver standards for MAS domain as well. The Foundation for Intelligent Physical Agents (FIPA) organization produced set of standards covering agent management, agent communication, and agent message transport.1 Within the FIPA organization the agent unified modeling language (AUML) initiative was established to leverage UML for modeling of large-scale agent-based applications [13]. The FIPA standardization efforts were aimed namely at software agents as autonomous entities communicating by using specific agent communication language (ACL) and operating on a higher decision making level or high-level control (HLC) in the case of control/manufacturing tasks. FIPA standardization efforts together with the progress in the object-oriented software technology strongly influenced the concept of agent-oriented software engineering (AOSE) providing various software development and programming methodologies. Let’s mention e.g., DESIRE [14], GAIA [15], SODA [16] or ADEPT [17] as typical representatives of such methodologies. The object-oriented approach is naturally suited for designing multiagents systems and as such the object-oriented languages like C++ and Java are commonly used for their development. So called agent platforms were created in order to provide the developer with the support for designing and executing the agent systems. The platform addresses specific features of multiagent systems such as message-based communication, interaction protocols, service registration and look-up, agent state and behaviors, etc. Variety of platforms, 1[Online]

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Fig. 2. “CWS”—agent-based control of U.S. Navy ship’s cooling system.

either open-source or commercial ones, were developed such as the most popular JADE [18], FIPA-OS [19], AGLOBE [20], MAdkit [21], JACK [22], and many others. The feature comparison and performance evaluation of the platforms from the industrial deployment perspective is presented in [23]. To ensure real-time responsiveness of the agent-based control system the architecture called holonic agent has been introduced. It is a compound object containing a low-level control (LLC) part processing the real-time data from sensors and actuators and a high-level control (HLC) part embodied by the agent [24]. LLC is implemented in languages of IEC 61131 standard for programming the Programmable Logic Controllers (PLCs) and runs directly on PLC. The communication interface between LLC and HLC is based on sharing the data directly in PLC memory in so called tags [24], or by using COM/DCOM technologies [25], [26] or OPC [27]. There is a unique solution that enables to run the C++ based agents together with IEC1131 control programs directly on the standard Rockwell Automation PLC of ControlLogix/CompactLogix line [24]. It utilizes modified PLC firmware and the Autonomous Cooperative System (ACS) platform to allow for distributing agents over several PLCs, register, search, and consume services and interact with low-level control. B. Applications In the past, the applications of agents in industry reported in the literature were centered in the following fields: automo-

tive, logistics, production planning and scheduling, and manufacturing control. One of the earliest industrial agent projects was undertaken in the mid-90s, when BHP Billiton in Melbourne asked Rockwell Automation to develop a solution that would increase the utilization of steel milling process. The key point was to dynamically assign jobs to available rolling stands and cooling boxes instead of using a predefined set of equipment for particular recipe as before. The proposed system turned out to perform better than the existing one, yet due to the concerns of possible damage to equipment it was not let to directly control the process but instead just recommended the optimal configurations to the operator [24]. Around 2000 Rockwell Automation started another project aimed at the application of multiagent systems for real-time control purposes. The initiative came from U.S. Office for Naval Research, which was looking for a robust and fault tolerant control of the U.S. Navy ship’s cooling system. The key requirement was provide a solution that ensures cooling system functionality in cases of severe failures or damages of the equipment. The agent-based system was designed, in which each onboard equipment, such as cooling unit, valve and load, is governed by an autonomous agent. Agents dynamically negotiate about provisioning of cold water and its transport through the redundant piping system (see Fig. 2). A unique feature is the ability to detect and isolate leakage and subsequently to reconfigure the control system in terms of


finding the alternative routing of water in order to continue in cooling of the critical ship systems [24]. In the aerospace domain, the National Aeronautics and Space Agency (NASA) used autonomous agents to balance multiple demands in satellites, such as staying on course, keeping experiments running, and dealing with the unexpected, thereby avoiding waste [28]. In 2000, another early application of industrial agents, known as Production 2000+, was installed in a factory plant of Daimler Chrysler producing engine cylinder heads [26]. The agent-based system was in day-to-day operation for five years, up to the end of the life-cycle of the targeted product, with a reported increase of 20% in productivity. In the same year, the experimental distributed scheduling and control simulation system was built by Brennan [25]. The job agent carries out the auction-based bidding with station agents to allocate resources needed for job’s operations. Later on in 2002, multiagent based ExPlanTech system developed at the Czech Technical University in Prague was introduced as the tool providing support of long-term production planning processes. It was successfully deployed by the CertiCon, a.s. company at LIAZ Pattern Shop producing patterns and forms for automobile industry [29] as well as in SKODA Auto, Mlada Boleslav for scheduling in the engine assembling workshop. In 2003, a PROSA-based agent system was installed for production control of a semiconductor wafer fabrication facility, entitled FABMAS (FAB Multiagent System) [30]. The Holonic Packing Cell developed at University of Cambridge’s Center for Distributed Automation and Control provided a large-scale industrial testbed for experiments with distributed intelligent control systems [27]. The dynamic resource allocation is ensured by belief-desire-intention (BDI) agents implemented in JACK platform. Magenta Technology developed the agent-based system Ocean i-scheduler for the Tankers International in 2005. The system schedules in real-time cargo assignment to vessels in a very large crude carrier fleet (46 units) used to carry out transcontinental transportations of oil [31]. Authors in [32] present the framework called ABAS oriented towards 3-D simulation and visualization of assembly process carried out by autonomous mechatronic devices represented by intelligent agents. Whitestein Technologies has developed an agent-based solution for real-time transport optimization for the European logistics company ABX Logistics based on the Living Systems/Adaptive Transport Network (LS/ATN). The potential overall cost saving was 11.7%, with an improvement of around 30% in the process efficiency [33]. The Agentsteel system presented in [34] establishes a generic agent-based solution for planning and observation of the overall steel production process. It was deployed inside the steelwork Völklingen of Saarstahl AG for computing the daily schedules. Several large-scale R&D initiatives were conducted in order to research and promote use of multiagent systems in manufacturing domain. AGENTLINK III2 was a coordination network with a focus on unifying agent-based activities in the different 2[Online]

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domains in order to put Europe at the leading edge of international competitiveness in this area. PABADIS-PROMISE3 focused the development of a new control design methodology based on distributed intelligence enabling fast, flexible, and efficient manufacturing. MASCADA4 was aimed at the development of agent-based manufacturing control systems that are able to manage production change and disturbance both effectively and efficiently, safeguarding and/or maximizing the production systems throughput. RI-MACS was focused on the development of open approaches with the use of multiagent systems, wireless technology and virtual engineering design methods to make manufacturing plants and equipment agile and reconfigurable [35]. Nearly all the industrial solution of the early period played the role of the large-scale demonstrators and only a few of them were really introduced into every day operation. These demonstrators documented the efficiency and potential of the agentbased solutions, but were very far from what was expected by the end users. The main obstacles of broader real-life industrial deployments of the agent based systems was namely lack of development tools and integration methodologies and lack of autonomous deliberative agents’ “thinking”—the solutions were simply hard-wired in the sense that all their patterns of behavior including the system reconfigurations tasks were preprogrammed by developers. Any change required a lot of additional programming and involvement of highly-skilled experts. The knowledge structures used in the agents were too static, cumbersome and poor. The introduction of the concept of social knowledge [36] represents the first effort to create structures supporting dynamic representation, maintenance and deployment of knowledge. But further enhancements in flexibility, robustness, autonomy of operation and scalability did require quite introduction of new ideas and technologies. III. PRESENT After the initial phase of using industrial agents, a new era came that did not bring just enhancement of the already existing technologies and techniques, but it has been stimulated by deployment of semantics and ontologies, leveraging of service-oriented architectures, and development of simulation capabilities. A. Architectures and Methods The compound holonic agent architecture with wrapped lowlevel and high-level control modules introduced in the previous time period proved to be viable till present. The advancement of the original concept is seen in the gradual replacement of IEC 61131 standard with a modern IEC 61499 suit for programming of LLC modules. The IEC 61499 brings the distributed way of designing the real-time control applications and thus better suits the overall character of industrial agent solutions. With this change a modernized interface between LLC and HLC (agents) came forth. It uses the dedicated service interface function blocks to exchange messages between the two control mod3[Online]

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ules [37]. In addition, the LLC is designed in such a way it ensures the common or at least minimal real-time control functionality even when the HLC is unavailable or has failed. In [38] the authors present a heart beating mechanism in IEC 61499, by which LLC periodically monitors agent vitality and if it does not receive response it can trigger the agent restart. The period around the year of 2005 represents next significant milestone in the evolution of technologies for intelligent manufacturing. The attention of researchers started to shift on two technologies coming from the IT world—the semantic web and service-oriented architectures (SoAs). In fact, this shift did set the trend also for the future of intelligent manufacturing systems as discussed in Section IV. The vision of Semantic web is to promote a common framework that allows the machines to search, interpret and share data on web. Achieving this vision requires that the sources of information are semantically structured. The terms like semantics, ontologies and metadata are often used interchangeably in this context. Basically, the semantics is captured by ontology, which provides a vocabulary for describing concepts, their relations, and constraints in given scope of interest. Ontologies have become widely used also for semantic description of knowledge related to production systems [39]. Basically said, ontology describes particular domain in terms of classes of objects (like machines, tools, operations, workers, orders, jobs, parts, etc.) and their relationships. Instead of having the interpretation of semantics implicitly embedded in the program code of agents, as was usual before, the agents now use ontologies to grasp the knowledge expressed in explicit format. It allows them to interpret it much effectively, reason about it, deduce new facts, etc. The new ontology-enhanced agent-based production control systems are designed in such a way that the agent system can absorb the real-time changes of the ontology with no need for reprogramming. For instance, the ontology can be extended with the new type of product and associated plan of production. Subsequently, the existing agents are able to process the new facts in ontology and start production of a new product. Even new machines needed for specific operations can be added on the shop floor. The respective agents register their services in the directory and from that time they can be discovered and their services used by others, again without a modification of the rest of the control system. Obviously, exploration of semantics and ontologies brings a totally new dimension in dynamic behavior of the MAS systems. There are attempts to establish generic manufacturing ontologies, such as MASON [40], NIST’s description of shop data model [41], automation objects [42], or OOONEIDA focusing on the infrastructure of automation components [43]. On the other hand, domain specific ontologies are developed to serve primarily the purposes of particular agent-based control system. It is the example of the OWO ontology for flexible manufacturing systems [44], the transport system ontology suggested in [45], the ontology for assembly line control described in [46], the ontology for agent-based reconfiguration of production processes [47], the ontology aimed at rent-a-car business [48], the FRISCO ontology designed to support organization of knowledge in automotive supply chains [49], or the ontology aimed at supply chain and logistic planning presented in [50].

Correspondingly, new agent architectures have been developed that apply ontologies as a main format for representation, storage, exchange and reasoning about knowledge. In the semantic MAST solution presented in [44] the behavior of agents focused on dynamic resource allocation is driven by the ontology that is used to describe the orders (requested product features, due dates, etc.) as well production processes (sequence of operations, requested material, etc.). Another ontology-based multiagent control architecture specifically targeted to material handling domain can be found in [51]. The agents employ situation model capturing the real state of the environment and activity model where activities, triggered by changes in situation model (for example sensor activation), tell the agent what future observations it should expect. Different internal models mentioned above represent the first steps towards the more complex world models reflecting the changes in the environment and helping to understand the current situation in its complexity, as basis for dynamic selection of the most appropriate behavior strategy/pattern. Such world models, e.g., presented in [52], contain not only the situation and activity models, data base of facts, but also database of expectations/observations. In [53] we can find not only a classical ontology of declarative items, but an ontology of explicitly specified agent’s behaviors which are described in procedural form. Such a type of ontologies enables to enrich the world model by a list of suitable behavioral patterns and to update it dynamically. SoA is an architectural paradigm for designing software in the form of reusable, loosely coupled and interoperable software components—services. SoA aims at defining interfaces, protocols, and data formats for accessing services in order to allow the developer to combine various services into a final application. Evidently, multiagent systems and SoA are based on the same principles—the application is composed of self-contained entities collaborating in a networked environment. As more and more business applications at enterprise level are implemented as service-oriented the first attempts to push the service-orientation also to lower layers of industrial automation architectures have been made. In the SOCRADES project for instance the aim is to exploit the SoA paradigm at both the business application level (MES and ERP systems) and the device level (smart I/Os, PLCs, etc.) [54]. Focusing on the device level the SOCRADES technology wraps the device-specific functionality as a service, which is exposed to other services in the factory. This can be seen as the evolution of the holonic agent architecture presented in previous section, where the device-level control functionality is wrapped with software agent, through which it is published and accessed by other agents. The other approach is the combination of features of both agents and services as described for example in [55], where the authors devise a concept of Service-oriented agents for collaborative production control systems. Agent-based simulation of agent-based industrial systems represents another key differentiator at the present stage. The agent-based systems cannot be—in principle—simulated in any different way. Only agent-based simulation allows emulation of the system behavior, studying its long-term stability and testing alternative solutions in a very safe way [56]. And, the lack of suitable simulation tools did represent a significant obstacle


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Fig. 3. “MAST”—agent-based simulation and control tool for discrete production.

to enhance the capabilities and efficiency of the agent-based solutions as well as trust in their operations. Real-life deployment of massive industrial multiagent systems is not a trivial task and it requires an elaborate rump-up phase which calls for complex testing, debugging, and simulation software tools. One of pioneering simulation tools reported in the literature was the Rockwell Automation’s MAST system [57], based on JADE platform originally developed for the purpose of simulation of material handling in flexible manufacturing (Fig. 3). It models basic components of the material handling systems like conveyor belts, diverters, AGVs, etc., and simulates communication among them with the goal to find an optimal transportation routing. The MAST system enables to emulate any component failure resulting in the relevant CNP-like negotiation processes. New components can be added, removed, or repaired on-the-fly, during the run-time operation. There has been a specific technology developed that enables switching the simulation into the real-time control carried out in standard industrial controllers. Thus, a smooth step-by-step shift from simulation to real-time control can be achieved. Another notable pioneering system is ABAS, developed by the Tampere University of Technology and Schneider Electric, as a tool aimed at simulation and visualization of the robot operations in the 3-D manufacturing space [32]. B. Applications Since 2005, the range of applications of the agent technology has changed, aiming toward logistics and transportation, manufacturing control, dynamic product routing, assembly, and military and defense. The industrial application of agents are now mainly driven by software companies, such as Magenta and NuTech complementing the initial efforts taken by automation technology providers like Rockwell Automation and Schneider Electric. Namely, Magenta Technology has implemented a series of multiagent applications aimed at real-time scheduling and logistics for different domains, like taxi scheduling implemented for the corporate Addison Lee taxi company

Fig. 4. “Smart Factory”—multiagent system for factory resource scheduling.

[58], routing and scheduling for the GIST logistics company [59], and rent a car optimization for Avis, U.K. [48]. These solutions introduce significant benefits, namely the decrease of idle miles, increase of driver utilization, increase of the fleet utilization and savings on fuel expenses. Cologne University of Applied Sciences in collaboration with Airbus, EADS, Knowledge Genesis, and other partners, prototyped a multiagent system for planning and scheduling of selected functionality of air-catering services [60]. Smart Solutions implemented full truck loads real-time scheduling for Prologics (Moscow, Russia) [61], and a multiagent system for real-time scheduling and optimization of workshop resources at Axion–Holding Izhevsk factory (Fig. 4), which is one of the biggest Russian electronics manufacturer. The system is being in its initial operation phase but it is expected to increase the workshop efficiency by 15%–20% [62]. A manufacturing execution system based on holonic concepts and JADE agent framework has been developed for the American Glass Product (AGP) company that produces laminated security glass for automotive application [63]. In the aerospace domain Smart Solutions in cooperation with Korolev Rocket and Space Corporation developed a multiagent flight and cargo

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Fig. 5. Flexible manufacturing system controlled by ADACOR-based multiagent control system.

planning system for the International Space Station (ISS) supporting the dynamic reschedule of amount of fuel and water, products for cosmonauts’ live support and some other goods that can be reallocated between spaceships flights [64]. University of Castilla–La Mancha prototyped a distributed decision support system for the airport ground handling management that combines the use of multiagent systems and wireless sensor networks [65]. The solution was tested at the Ciudad Real Central Airport in Spain. In laboratorial environment, numerous agent-based applications were reported in the literature. It is for instance the application of a multiagent control system for shop floor assembly in the Novaflex manufacturing test bed at Uninova, Portugal [46]. Another example is the implementation of a multiagent manufacturing control system following the ADACOR holonic architecture in a real laboratorial flexible manufacturing system at the Polytechnic Institute of Bragança, Portugal [66] (Fig. 5). Authors in [67] introduce the iShopFloor concept that focuses the implementation of distributed intelligence in the manufacturing shop floor, using intelligent agents, specifically to achieve distributed manufacturing scheduling. Rockwell Automation’s MAST real-life control capabilities have been verified on the palette transport system at the ACIN lab at the Vienna University of Technology [57]. The semantic extension of MAST aimed at dynamic resource allocation has been demonstrated on a simulated holonic packing cell scenario [44]. It has been shown that the system is able to absorb the description of a new type of product and pursue its production without changing the program code of agents. The cooperation between Rockwell Automation and Vienna University of Technology within the framework of EU OntoReA project resulted

in the development of ontology-based reflective world model for autonomous agents [52]. Authors in [68] have proposed an agent model for dynamic product routing based on stigmergic principles resembling the food foraging ant colonies. The ATG Group at the Czech Technical University has a wide portfolio of agent applications in the area of unmanned aerial vehicles (UAVs) and air traffic control. The AgentFly system provides for instance the ground tactical mission support by multiagent control of UAVs (including cooperative area exploration, cooperative moving target tracking, or surveillance) [69]. The other feature is the decentralized airplane collision avoidance based on the free flight concept when the airplane can flight freely according to its own objectives and separate its trajectory from other airplanes by negotiation [70]. There are several ongoing EU funded R&D projects aimed at the application of agent technology to industrial problems. ECOLEAD initiative leverages the principles of virtual enterprises with the aim at creation of foundations and mechanisms for collaborative and networked-based industry society in Europe [12]. GRACE5 applies agents for integration of process and quality control. The validation of the results will be conducted on the washing machine production line demonstrator. The IDEAS project6 applies the concepts of the Evolvable Assembly Systems in order to provide fault tolerant and reconfigurable production control. COSMOS project targets the assembly of wind turbines nacelles trying to establish the factory organization concept based on intelligent, self-adaptive factory units. SELF-LEARNING project7 uses highly reliable and se5[Online]

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Available: http://www.selflearning.eu


cure service-based self-learning solutions aiming the integration of control and maintenance of production systems. CONET8 aims at developing a community capable of conducting the research to achieve the vision of combining embedded systems for robotics and control, pervasive computing and wireless sensor networks. The primary objective of SOCRADES was to develop a platform for designing, executing and managing next-generation industrial automation systems, exploiting the SoA paradigm both at the device and at the business application level. At the device level the SOA infrastructure based on devices profile for web services (DPWS) was designed for encapsulating the intelligence and sensing/actuating skills as web services. Such service can be discovered and invoked by other networked devices or applications in a unified manner [54]. The IMC-AESOP project, apparently to follow-up of SOCRADES, investigates the application of SoA for monitoring and supervisory control of very large scale distributed systems in process control applications.9 The intention is to investigate how deep in the factory automation architecture it is possible to go with the SoA and if it is feasible to involve SoA in the process control loop at the device level [71]. The project will also try to define a migration path from legacy systems towards next generation of SoA-enabled SCADA/DCS systems. C. Workshops, Conferences and Technical Committees The topic of industrial agents is researched and discussed by the scientific and industrial communities in several international conferences, namely International Conference on Industrial Applications of Holonic and Multiagent Systems (HoloMAS), International Conference on Practical Applications of Agents and Multiagent Systems (PAAMS), International KES Conference on Agents and Multiagent Systems—Technologies and Applications (AMSTA) or IEEE International Conference on Industrial Informatics (INDIN). Additionally, several workshops and special sessions have been organized in the last years in several international conferences, notably the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Annual Conference of the IEEE Industrial Electronics Society (IECON), IEEE International Symposium on Industrial Electronics (ISIE), and IEEE International Conference on Emerging Technologies on Factory Automation (ETFA). A significant number of these events are organized under the scope of the IEEE IES Technical Committee on Industrial Agents10 that has the objective of providing a forum where researchers and application sector specialists can come together to continue the development and application of industrial agent technology in production, services and infrastructure sectors. The expected output of this effort is the contribution for a wider application of agent technology in industrial applications. The same mission has the Technical Committee on Distributed Intelligent Systems under IEEE System, Man, and Cybernetics Society. 8[Online]

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IV. FUTURE In this section an attempt is made to give an outlook for exploration of agent-related technologies in industrial sector. First, future trends are identified along with pointing out the main evolutionary steps of the technology to be expected. Second, the prospective application fields are discussed. A. Future Trends Real-time control still remains a greatest challenge [72] because the deployment of agent-based or service-oriented systems requires the industry to make a radical change in the way control solutions have been designed, implemented and maintained for decades. It is necessary to fully explore the modular approach where the control modules, being in charge of control of particular physical equipment, comprise of both the low-level, real-time control block, and the high-level, intelligent software agent entity. To ensure conceptual compatibility it is necessary for low-level control to move from procedural, taskoriented, and controller-centric programming to object and/or service oriented programming. There were attempts to extend the ladder diagram of IEC61131 to provide some object-oriented features, like inheritance, iteration over lists of instances, etc. However, the configuration and runtime environments for PLCs do not support such features and thus there have to be separate configuration tools ensuring subsequent conversion of the object model into the default IEC 61131 languages [73]. The role of the IEC 61499 standard here is envisioned to grow as it inherently supports modularity, object orientation, and easy reconfiguration [74]. There is a big research community around it, the mature configuration and runtime tools already exist, like 4DIAG11 and there are first pilot deployments of runtimes in commercial embedded controllers. Even though the trend goes to service-orientation it is highly probable that the real-time scan-based functionality will be even in the future ensured by the relevant industrial standards, whether it is IEC61131 or IEC61499, encapsulated within a service. Such architecture will enable the adoption of a unifying technology for all levels of enterprise, from sensors and actuators to the enterprise business applications, and thus contribute to both the vertical and horizontal integration [71]. Although the PLCs are still the state-of-the-art, the large industrial automation vendors envisage the significance of service-orientation for their future businesses, as documented for instance by participation of Schneider Electric and Siemens in discussed SOCRADES and IMC-AESOP projects. Complementary to this there is an evolution concerning the execution platform hosting the agents. In past agents were executed solely on PCs, then there were attempts to run them directly in PLCs [73]. With the boom of new generations of processors leveraging the ARM architecture and variety of free operating systems based on Linux it could be envisioned that new generation of embedded controllers will come forth and provide suitable runtime environment for both the agents/services and the low-level control. Such idea is for instance pushed forward within the IMC-AESOP project, which aim is to investigate applicability of SoA for process monitoring and control. Part of 11[Online]

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the investigation is the effort of improving the performance of SoA at the device level from 10 ms, as achieved during the SOCRADES, to 1 ms, which is enough for real-time requirements of process control applications. The solution includes the implementation of the Device Profile for Web Services (DPWS) stack and replacement of inefficient XML/SOAP message encoding with the binary Efficient XML Interchange (EXI) format [75]. The authors conclude that the first performance measurements show promising results but confess that improving the performance ten times is still an unachievable challenge. Convergence of the agent-based technologies and the service oriented architectures is quite visible and will surely represent an important future trend. The SoA research has focused on developing standards for well-defined interfaces, workflows, and protocols. Services are traditionally transient and stateless process that are instantiated when invoked and exist only during execution. In contrast, agents are persistent entities that perform their tasks proactively in pursuit of their design objectives, are socially and environmentally aware, exhibit adaptive behavior to changing context, employ knowledge models allowing them to perform intelligent reasoning, can decompose tasks and dynamically delegate the execution of subtasks to other agents rather than blindly following a prescribed workflow, etc. [76]. Together with the fact that a very little focus in SoA research was on the mechanisms that help the service to perform its task it is apparent that the SoA world can significantly benefit from leveraging some of the agent attributes. Especially in the industrial environments where the control software operates within the physical context and under changing conditions some sort of combination of agents and services will be needed. Some of the first trials are already available such as in [77], where the authors devise the term service-oriented agents for collaborative production control systems. Web applications and clouds are entirely changing the way software applications are designed and delivered to the end user. The user only needs the modern web browser to run the application instead of installing and maintaining software on his/her machine, bothering with incompatible versions of libraries, etc. The vision is that the latest technologies for developing web applications like HTML5, CSS3, AJAX, or JavaScript will be adopted for establishing next generation of SoA- and cloudbased agent applications. One of the benefits is the wide portability as the agent applications will be enabled on variety of devices including mobile phones and tablets. Regarding tablets the vision is that they will become constituent of SCADA (Supervisory Control and Data Acquisition) alternating the classical operator panels. Embedding agents as inherent part of web application running on tablet might enhance the capabilities of such device so that it might not only visualize the data but also execute some intelligent processing. Simulation have played indispensable role for verification of agent-based control system behavior before its real deployment. There is growing number of applications like for instance air traffic control or production planning and logistics where simulation is used during the system run to provide valuable data needed for the real-time decisions. As simulation can run in accelerated time it can help to simulate and evaluate multiple alternative scenarios of the future evolution of the

real system. Consequently, the best strategy can be applied in real time considering the simulation results. Obviously, agents provide a suitable paradigm for simulating highly complex systems comprising of interacting entities. Quite important is to continue to study methods and techniques for direct switching from agents’ simulation to agent-based control in the rump-up period. These techniques are expected to enable an efficient co-existence and symbiosis of simulation and execution. In general, the agents should behave in a more intelligent way. The first step towards this goal is their better perception and understanding of the manufacturing/operation situation and circumstances. Their internal world models should become much richer and much more flexible. The result of the AI research, especially in the fields of knowledge representation, ontology, semantics, machine learning, and decision making should contribute significantly. One of the main attributes associated to intelligence of systems is the capability to learn exhibited by the system. Learning can be defined as a way to acquire knowledge and skills to respond to the dynamic evolution of the environment and to improve the system ability to act in the future [78]. According to [79] learning in a multiagent environment can help agents to improve their performance, namely improving the ability to behave optimally in the future to achieve goals, and to react to unexpected events by generalizing what they have learned during a training stage or a period of its own, continuing operation. Depending on the situation where learning is applied, different learning techniques may be applied, ranging from trivial memorization of experience to the creation of entire scientific theories. Several authors have been using the machine learning methods in the industrial multiagent solutions with success, namely in process planning [80], flow shop scheduling [9] and air traffic management [81], but we do expect much more intensive research in this field in the near future. The standardization issue is persistently pointed out by industry community as a major challenge for the industrial acceptance of the agent technology. In particular a set of standards is already affecting the development of industrial agentbased solutions, besides the already mentioned standards, like IEEE FIPA, IEC 61499, and IEC 61131-3. As example, the following standards are expected to receive increasing importance for agent-based industrial solutions. • ISA 95—a standard for the integration of enterprise and control systems, that provides the basis for the development of standard interfaces between ERP and MES systems. This standard is applied to all industries, and to all sorts of processes, like batch processes, continuous and repetitive processes. • ISA 88—a standard addressing batch process control that defines the physical model, procedures, and recipes. • IEC 61850—a recently introduced standard for communication networks and systems for power utility automation [82] which very well complements the IEC 61499 standard for low-level automation [83]. • OPC UA—an evolution of OLE for process control (OPC), is an emerging standard aimed mainly at the communication between controllers and higher layers of industrial architectures like SCADA, MES, or ERP.


The goal of OPC UA is to move from the original OPC communication models (COM/DCOM), tightly linked with Microsoft Windows architecture, to cross-platform SoA. Since the OPC UA will soon become a standard in the industry world it will also influence the SoA-based architectures for intelligent manufacturing. Within the IMC-AESOP project there are attempts to combine OPC UA with DPWS to achieve a common data model for SoA based communication among embedded devices [83]. The standardization issues should be seen in two different perspectives: on one hand, the industrial agent-based solutions should fulfill the current related industrial standards, but on the other hand it should drive the introduction of new standards or influence the specifications of the existing standards. As an example of this last perspective is the joint collaboration with the FIPA standardization body to accommodate some particular requirements of agent technology to be used in industrial applications. In fact, the need to incorporate special hard requirements associated to industrial environments in the FIPA specification, namely, the no preemption of operations, the event notification, the unsubscription of services, the appropriate protocols for manufacturing domain and mechanisms for the integration of physical manufacturing devices are required. B. Application Fields Based on the authors’ experiences and visions this section lists the prospective application domains that might benefit from employment of agent technologies. • Production planning, supply chain, and logistics—Production scheduling, resource allocation, dynamic product or vehicle routing, management of virtual organizations, etc. • Traffic control—Optimization of the flow of traffic in complex environments and situations, including intelligent transportation, car-to-car communication, air-traffic control, etc. • Energy and smart grids—Monitoring and management of large-scale networks of energy producers and consumers. It is one of the EU priorities for 2020 and beyond to develop a new integrated European energy network meeting the increasing demand, balancing generation from renewable sources, supporting new electricity storage technologies, etc. This will not be possible without intelligence at generation, transmission and consumption level. • Buildings and home automation—Monitoring and management of a network of distributed automation devices in intelligent homes. • Military, defense and humanitarian relief applications—Control and coordination of multirobot teams (aerial, ground, underwater vehicles) in variety of tasks such as surveillance, patrolling, securing transits, unknown area exploration, etc. • Network security—Distributed network traffic analysis, detection of attacks, etc. A special topic to be intensively investigated will be the security and “privacy” of agent communication within the frame of agent-based industrial solutions.

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V. CONCLUSIONS The intention of the paper was to monitor the chronology of applications of multiagent system concepts in the industrial automation domain and to identify the key milestones. There are areas where agents have been successfully deployed and other areas where they have not broken through. The former one is the production planning, scheduling, and logistics where agents bring measurable benefits in terms of better resource utilization, shorter delivery times, fuel savings, etc. Section III-B provides a long list of real customers, including a taxi company, a rent-a-car company, a logistics provider, a producer of consumer electronics, or an air catering services provider, where agent-based systems are deployed for day-to-day scheduling operations. The latter one is the factory automation, where agent-based systems are still deployed only in laboratorial environments or as industrial prototypes. There are still barriers, either technology- or human factor-related, that obstruct the adoption of these novel paradigms at large scale. Interestingly, both the industrial agents community and the community around service-oriented automation, have identified the identical issues. The major technological roadblock is the inability of the new technology to respect contemporary industrial requirements for real-time capabilities, robustness, availability of mature engineering tools, safety, and standardization. The SOCRADES project roadmap [54] for instance notes that the technology runs well on PCs, but when it comes to its deployment to resource-constrained devices it suffers from low performance. Additionally, the technologies are also not robust enough to be implemented in real production systems and the SOA-based standards for safety applications are still missing. From the human factor point of view the key issue is that the radical paradigm shift is needed from controller-centric view to modularization and service orientation. The extensive education of engineering as well as managerial people would be required to comprehend the new technology and increase the trust in its capabilities. In order to cope with these issues the research community has to address the following aspects. • Provide demonstrators running in industry, showing improved performance of agent- or SoA-enabled solutions. This allows the companies to start “believing” in the new technology and feeling the urgent need to implement these principles. Especially, it is important to address the strict real-time requirements and prove that the new technologies are ready for use in real industrial environments. During the AESOP project some of the first experiments with SoA application at the device level have shown promising results regarding the performance of DPWS running on an ARM9-based embedded platform [75]. • Provide return of investment (ROI) analysis, considering the costs associated to the development, operation and maintenance of the agent-based solutions. • Provide agent-based enabled solutions as black boxes, with transparent interfaces and easy configuration tools. In fact, the complexity of the agent-based solution should be hidden for the user, as it occurs in our cars or washing

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machines, being only necessary for the user to know the product characteristics and how to configure it. In the past, the development of agent-based systems was spread by U.S., Japan, Europe, and Australia, mainly due to the efforts of the IMS program. However, nowadays, the main efforts are concentrated in Europe. The list of R&D projects funded by EU is a good indicator of the importance of this technology to improve the competitiveness of European manufacturing industry. The EU Factories of Future initiative calls for ICT-based production systems and high quality manufacturing technologies capable of optimizing their performance with a high degree of autonomy and adaptability for a balanced combination of high throughput and high accuracy production. We believe that the basic concepts of multiagent systems combined with the modern software technologies like service oriented architectures and semantic web will address this challenge and will help to make this idea come through. ACKNOWLEDGMENT The authors would like to thank all of the members of the IEEE IES Technical Committee on Industrial Agents for their contribution in the on-going activities and particularly in collecting and commenting the industrial agent-based applications. REFERENCES [1] M. Wooldridge, An Introduction to Multiagent Systems. Hoboken, NJ: Wiley, 2002. [2] M. Wooldridge and N. R. Jennings, “Intelligent agents: Theory and practice,” Knowl. Eng. Rev., vol. 10, no. 2, pp. 115–152, 1995. [3] Function Blocks—Part 1–4, IEC 61499, Int. Electrotechnical Commission, 2005. [4] A. Koestler, The Ghost in the Machine. London, U.K.: Arkana Books, 1969. [5] J.-L. Chirn and D. C. McFarlane, “Building holonic systems in today’s factories: A migration strategy,” J. Appl. Syst. Studies, vol. 2, no. 1, 2001. [6] H. V. Brussel, J. Wyns, P. Valckenaers, and L. Bongaerts, “Architecture for holonic manufacturing systems: PROSA,” Comput. Ind., vol. 37, no. 3, pp. 255–274, 1998. [7] P. Leitão, A. W. Colombo, and F. Restivo, “ADACOR: A collaborative production automation and control architecture,” IEEE Intell. Syst., vol. 20, no. 1, pp. 58–66, Jan./Feb. 2005. [8] J.-L. Chirn and D. McFarlane, “A holonic component-based approach to reconfigurable manufacturing control architecture,” in Proc. Int. Workshop Ind. Appl. Holonic Multiagent Syst., 2000, pp. 219–223. [9] V. D. Parunak, A. D. Baker, and S. J. Clark, “The AARIA agent architecture: An example of requirements-driven agent-based system design,” presented at the Proc. 1st Int. Conf. Auton. Agents, Marina del Rey, CA, 1997. [10] F. Maturana, W. Shen, and D. H. Norrie, “MetaMorph: An adaptive agent-based architecture for intelligent manufacturing,” Int. J. Prod. Res., vol. 37, no. 10, pp. 2159–2174, 1999. [11] P. Valckenaers, H. V. Brussel, J. Wyns, P. Peeters, and L. Bongaerts, “Multi-agent manufacturing control in holonic manufacturing systems,” in Human Management Systems. Amsterdam, The Netherlands: IOS Press, 1999, vol. 18. [12] L. M. Camarinha-Matos, H. Afsarmanesh, N. Galeano, and A. Molina, “Collaborative networked organizations—Concepts and practice in manufacturing enterprises,” Comput. Ind. Eng., vol. 57, no. 1, pp. 46–60, 2009. [13] B. Bauer, J. P. Müller, and J. Odell, Agent UML: A Formalism for Specifying Multiagent Software Systems, ser. AOSE 2000, LNCS 1957, P. Ciancarini and M. J. Wooldridge, Eds. Berlin, Germany: SpringerVerlag, 2011, pp. 91–103. [14] F. M. Brazier, B. D. Keplicz, N. Jennings, and J. Treur, “DESIRE: Modeling multiagent systems in a compositional framework,” Int. J. Cooperative Inf. Syst., vol. 6, pp. 67–94, 1997.

[15] M. Wooldridge, N. J. Jennings, and D. Kinny, “The GAIA methodology for agent-oriented analysis and design,” J. Auton. Agents MultiAgent Syst., vol. 3, no. 3, pp. 285–312, 2000. [16] A. Omicini, SODA: Societies and Infrastructures in the Analysis and Design of Agent-based Systems, ser. Lecture Notes in Computer Science, P. Ciancarini and M. J. Wooldridge, Eds. Heidelberg, Germany: Springer, 2001, vol. 1957, pp. 185–193. [17] N. R. Jennings, T. J. Norman, P. Faratin, P. O’Brien, and B. Odgers, “Implementing a business process management system using ADEPT: A real-world case study,” Appl. Artif. Intell. J., vol. 14, no. 5, pp. 421–490, 2000. [18] F. Bellifemine, G. Caire, and D. Greenwood, Developing Multi-Agent Systems with JADE. New York: Wiley, 2007. [19] S. Poslad, P. Buckle, and R. Hadingham, “The FIPA-OS agent platform: Open source for open standards,” in Proc. 5th Int. Conf. Exhib. Practical Appl. Intell. Agents Multiagents, 2000, pp. 355–368. [20] D. Šišlák, M. Rehák, M. Pěchouček, M. Rollo, and D. Pavlíček, “A-Globe: Agent development platform with inaccessibility and mobility support,” in Software Agent-Based Applications, Platforms and Development Kits. Berlin: Birkhauser Verlag, 2005, pp. 21–46. [21] O. Gutknecht and J. Ferber, “The MADKIT agent platform architecture,” in Proc. Agents Workshop Infrastructure Multi-Agent Syst., 2000, pp. 48–55. [22] N. Howden, R. Ronnquist, A. Hodgson, and A. Lucas, “JACK intelligent agents—Summary of an agent infrastructure,” in Proc. 5th ACM Int. Conf. Auton. Agents, 2001, pp. 251–257. [23] P. Vrba, “MAST: Manufacturing agent simulation tool,” in Proc. IEEE Conf. Emerging Technologies and Factory Automation, Lisbon, Portugal, Sep. 2003, vol. 1, pp. 282–287. [24] V. Mařík, P. Vrba, K. Hall, and F. Maturana, “Rockwell automation agents for manufacturing,” in Proc. Ind. Track 4th Int. Joint Conf. Auton. Agents Multiagent Syst. (AAMAS), Utrecht, The Netherlands, 2005, pp. 107–113. [25] R. W. Brennan and W. O, J. A. Joines, R. R. Barton, K. Kang, and P. A. Fishwick, Eds., “A simulation test-bed to evaluate multi-agent control of manufacturing systems,” in Proc. Winter Simulation Conf., Orlando, FL, 2000, pp. 1747–1756. [26] S. Bussmann and K. Schild, “An agent-based approach to the control of flexible production systems,” in Proc. 8th IEEE Int. Conf. Emerging Technol. Factory Automat., 2001, vol. 2, pp. 481–488. [27] M. Fletcher, D. McFarlane, A. Thorne, D. Jarvis, and A. Lucas, V. Mařík, D. McFarlane, and P. Valckenaers, Eds., “Evaluating a holonic packing cell,” in Proc. HoloMAS 2003, 2003, pp. 246–257, LNAI 2744, Springer-Verlag Berlin Heidelberg. [28] S. Das, P. Gonsalves, and R. Krikorian, “Multi-agent planning and scheduling environment for enhanced spacecraft autonomy,” in Proc. 5th Int. Symp. Artif. Intell., Robotics Automat. Space, 1999, pp. 91–98. [29] M. Pěchouček, A. Říha, J. Vokřínek, V. Mařík, and V. Pražma, “ExPlanTech: Applying multi-agent systems in production planning,” Int. J. Production Research, vol. 40, no. 15, pp. 3681–3692, 2002. [30] L. Monch, M. Stehli, and J. Zimmermann, FABMAS: An Agent-Based System for Production Control of Semicondutor Manufacturing Process, ser. Lecture Notes in Artificial Intell., V. Marík, D. McFarlane, and P. Valckenaers, Eds. Heidelberg, Germany: Springer-Verlag, 2003, vol. 2744, pp. 258–267. [31] J. Himoff, P. P. Skobelev, and M. Wooldridge, “Magenta technology: Multi-agent systems for industrial logistics,” in Proc. 4th Int. Conf. Auton. Agents Multiagent Syst. (AAMAS), 2005, pp. 60–66. [32] J. L. M. Lastra, E. L. Torres, and A. W. Colombo, A 3D Visualisation and Simulation Framework for Intelligent Physical Agents, V. Mařík, W. Brennan, and M. Pechoucek, Eds. Heidelberg, Germany: Springer, 2005, pp. 23–28. [33] K. Dorer and M. Calisti, “An adaptive solution to dynamic transport optimization,” in Proc. 4th Int. Conf. Auton. Agents Multiagent Syst., 2005, pp. 45–51. [34] S. Jacobi, C. Madrigal, E. Leon-Soto, and K. Fischer, “AgentSteel: An agent-based online system for the planning and observation of steel production,” in Proc. 4th Int. Joint Conf. Auton. Agents Multiagent Syst. (AAMAS), 2005, pp. 114–119. [35] R. Checcozzo et al., “RI-MACS: An innovative approach for future automation systems,” Int. J. Mechatron. Manuf. Syst., vol. 2, no. 3, pp. 242–264, 2009. [36] V. Mařík, O. Štěpánková, and M. Pěchouček, “Social Knowledge in Multiagent Systems,” in Mutliagent Systems and Applications, ser. LNAI 2086. Heidelberg, Germany: Springer, 2001, pp. 211–245.


[37] W. Lepuschitz, M. Vallée, M. Merdan, P. Vrba, and J. Resch, “Integration of a heterogeneous low level control in a multi-agent system for the manufacturing domain,” in Proc. IEEE Conf. Emerging Technol. Factory Automat., 2009, pp. 7–14. [38] I. Hegny, O. Hummer-Koppendorfer, A. Zoitl, G. Koppensteiner, and M. Merdan, “Integrating software agents and IEC 61499 realtime control for reconfigurable distributed manufacturing systems,” in Proc. Int. Symp. Ind. Embedded Syst., 2008, pp. 249–252. [39] S. Runde and A. Fay, “Software support for building automation requirements engineering—An application of semantic web technologies in automation,” IEEE Trans. Ind. Inf., vol. 7, no. 4, pp. 723–730, Nov. 2011. [40] S. Lemaignan, A. Siadat, J.-Y. Dantan, and A. Semenenko, “MASON: A proposal for an ontology of manufacturing domain,” in Proc. IEEE Workshop Distrib. Intell. Syst., 2006, pp. 195–200. [41] Shop Data Model and Interface Specification, ser. NISTIR, 7198. Gaithersburg, MD: U.S. Dept. Commerce, Technol. Admin., Nat. Inst. Standards Technol., 2005 [Online]. Available: http://www.worldcat. org/title/shop-data-model-and-interface-specification/oclc/59671756 [42] O. Lopez and J. L. M. Lastra, “Using semantic web technologies to describe automation objects,” Int. J. Manuf. Res., vol. 1, no. 4, pp. 482–503, 2006. [43] V. Vyatkin, J. Christensen, and J. Lastra, “OOONEIDA: An open, object-oriented knowledge economy for intelligent industrial automation,” IEEE Trans. Ind. Inf., vol. 1, no. 1, pp. 4–17, Feb. 2005. [44] P. Vrba, M. Radakovič, M. Obitko, and V. Mařík, “Semantic technologies: Latest advances in agent-based manufacturing control systems,” Int. J. Prod. Res., vol. 49, no. 5, pp. 1483–1496, 2011. [45] M. Merdan, G. Koppensteiner, I. Hegny, and B. Favre-Bulle, “Application of an ontology in a transport domain,” in Proc. IEEE Int. Conf. Ind. Technol., Chengdu, China, 2008, pp. 1–6. [46] G. Cândido and J. Barata, A Multiagent Control System for Shop Floor Assembly, V. Mařík, V. Vyatkin, and A. W. Colombo, Eds. Berlin, Germany: Springer-Verlag, 2007, pp. 293–302. [47] Y. Al-Safi and V. Vyatkin, V. Mařík, V. Vyatkin, and A. W. Colombo, Eds., “An ontology-based reconfiguration agent for intelligent mechatronic system,” in Proc. HoloMAS, 2007, pp. 114–126. [48] M. Andreev, G. Rzevski, P. Shviekin, P. Skobelev, and I. Yankov, A Multi-agent Scheduler for Rent-a-Car Companies, V. Mařík, T. Strasser, and A. Zoitl, Eds. Berlin, Germany: Springer-Verlag, 2009, pp. 305–314. [49] B. Hellingrath, M. Witthaut, C. Böhle, and S. Brügger, An Organizational Knowledge Ontology for Automotive Supply Chains, V. Mařík, T. Strasser, and A. Zoitl, Eds. Berlin, Germany: Springer-Verlag, 2009, pp. 37–46. [50] M. Andreev, G. Rzevski, P. Skobelev, P. Shveykin, A. Tsarev, and A. Tugashev, Adaptive Planning for Supply Chain Networks, V. Mařík, V. Vyatkin, and A. W. Colombo, Eds. Berlin, Germany: SpringerVerlag, 2007, pp. 215–224. [51] M. Vallee, M. Merdan, W. Lepuschitz, and G. Koppensteiner, “Decentralized reconfiguration of a flexible transportation system,” IEEE Trans. Ind. Inf., vol. 7, no. 3, pp. 505–516, Mar. 2011. [52] W. Lepuschitz, A. Zoitl, M. Vallée, and M. Merdan, “Towards selfreconfiguration of manufacturing systems using automation agents,” Trans. Syst., Man, Cybern., C: Appl. Rev., vol. 41, no. 1, pp. 52–69, Jan. 2011. [53] M. Radakovič, M. Obitko, and P. Vrba, “Architecture for explicit specification of agent behavior,” in Proc. 13th IFAC Symp. Inf. Control Problems Manuf., Moscow, Russia, Jun. 2009. [54] M. Taisch, A. W. Colombo, S. Karnouskos, and A. Cannata, “SOCRADES roadmap: The future of SOA-based factory automation,” 2010. [Online]. Available: http://www.socrades.eu/ [55] G. Cândido, A. W. Colombo, J. Barata, and F. Jammes, “Service-oriented infrastructure to support the deployment of evolvable production systems,” IEEE Trans. Ind. Inf., vol. 7, no. 4, pp. 759–759, Nov. 2011. [56] V. Mařík and D. C. McFarlane, “Industrial adoption of agent-based technologies,” IEEE Intell. Syst., vol. 20, no. 1, pp. 27–35, Jan./Feb. 2005. [57] P. Vrba, V. Mařík, and M. Merdan, “Physical deployment of agentbased industrial control solutions: MAST story,” in Proc. IEEE Int. Conf. Distrib. Human-Mach. Syst., 2008, pp. 133–139. [58] A. Glaschenko, A. Ivaschenko, G. Rzevski, and P. Skobelev, “MultiAgent real time scheduling system for taxi companies,” in Proc. Int. Conf. Auton. Agents Multiagent Syst. (AAMAS), Budapest, Hungary, 2009, pp. 29–36.

2371

[59] A. Glashenko, J. Himoff, S. Inozemtsev, and P. Skobelev, “Case studies of magenta i-Scheduler for road transportation,” in Proc. 7th Int. Conf. Auton. Agents Multiagent Syst. (AAMAS), 2007, pp. 273:1–273:8. [60] U. Inden, G. Rzevski, P. O. Skobelev, I. A. Syusin, and A. V. Tsarev, “Multi-agent adaptive planning platform for complex airport services,” in Proc. 10th Int. Conf. Problems Control Complex Syst., Samara, Russia, 2008, pp. 399–413. [61] “Smart solutions,”, Samara, Russia, 2012. [Online]. Available: http:// www.smartsolutions-123.ru/en [62] M. Andreev, A. Ivaschenko, P. Skobelev, and A. Tsarev, “Multi-Agent platform design for adaptive networks of intelligent production schedulers,” in Proc. 10th Int. IFAC Workshop Intell. Manuf. Syst., 2010, pp. 78–83. [63] P. Blanc, I. Demongodin, and P. Castagna, “A holonic approach for manufacturing execution system design: An industrial application,” Eng. Appl. Artif. Intell., vol. 21, no. 3, pp. 315–330, 2008. [64] A. Ivaschenko, I. Khamits, P. Skobelev, and M. Sychova, Multiagent System for Scheduling of Flight Program, Cargo Flow and Resources of International Space Station, V. Mařík, P. Vrba, and P. Leitão, Eds. Berlin, Germany: Springer-Verlag, 2011, pp. 165–174. [65] P. Ansola, J. de las Morenas, A. García, and J. Otamendi, “Distributed decision support system for airport ground handling management using WSN and MAS,” Eng. Appl. Artif. Intell., vol. 25, no. 3, pp. 544–553, 2012. [66] P. Leitão and F. Restivo, “Implementation of a holonic control system in a flexible manufacturing system,” IEEE Trans. Syst., Man Cybern., C: Appl. Rev., vol. 38, no. 5, pp. 699–709, Sep. 2008. [67] W. Shen, S. Lang, and L. Lang, “iShopFloor: An internet-enabled agentbased intelligent shop floor,” IEEE Trans. Syst., Man Cybern., C: Appl. Rev., vol. 35, no. 3, pp. 371–381, May 2005. [68] Y. Sallez, T. Berger, and D. Tretensaux, “A stigmergic approach for dynamic routing of active products in FMS,” Comput. Ind., vol. 30, no. 3, pp. 204–216, 2009. [69] J. Vokřínek, P. Novák, and A. Komenda, V. Mařík, P. Vrba, and P. Leitão, Eds., “Ground tactical mission by multi-agent control of UAV operations,” in Proc. HoloMAS, 2011, pp. 225–234. [70] D. Šišlák, P. Volf, and M. Pěchouček, “Agent-based cooperative decentralized airplane collision avoidance,” IEEE Trans. Intell. Transp. Syst., vol. 12, no. 1, pp. 36–46, Mar. 2011. [71] S. Karnouskos, A. W. Colombo, F. Jammes, J. Delsing, and T. Bangemann, “Towards an architecture for service-oriented process monitoring and control,” in Proc. 36th Annu. Conf. IEEE Ind. Electron. Soc. (IECON), 2010, pp. 1385–1391. [72] M. Metzger and G. Polaków, “A survey on applications of agent technology in industrial process control,” IEEE Trans. Ind. Inf., vol. 7, no. 4, pp. 570–570, Nov. 2011. [73] P. Vrba, P. Tichý, V. Mařík, K. Hall, R. Staron, F. Maturana, and P. Kadera, “Rockwell automation’s holonic and multi-agent control systems compendium,” IEEE Trans. Syst., Man Cybern., C: Appl. Rev., vol. 41, no. 1, pp. 14–30, Jan. 2011. [74] V. Vyatkin, “IEC 61499 as enabler of distributed and intelligent automation: State-of-the-Art review,” IEEE Trans. Ind. Inf., vol. 7, no. 4, pp. 768–768, Nov. 2011. [75] F. Jammes, “Real time device level service-oriented architectures,” in Proc. 36th Annu. Conf. IEEE Ind. Electron. Soc., 2010, pp. 1385–1391. [76] T. Payne, “Web services from an agent perspective,” IEEE Intell. Syst., vol. 23, no. 2, pp. 12–14, Mar./Apr. 2008. [77] J. M. Mendes, P. Leitão, F. Restivo, and A. W. Colombo, Service-Oriented Agents for Collaborative Industrial Automation and Production Systems, V. Mařík, T. Strasser, and A. Zoitl, Eds. Berlin: SpringerVerlag, 2009, pp. 13–24. [78] W. Shen, F. Maturana, and H. D. Norrie, “Learning in agent-based manufacturing systems,” in Proc. AI Manuf. Res. Planning Workshop, 1998, pp. 177–183. [79] C. V. Goldman and J. S. Rosenschein, Mutually Supervised Learning in Multiagent Systems, G. Weiss and S. Sen, Eds. Berlin, Germany: Springer-Verlag, 1996, pp. 85–96. [80] H. G. Park and J. M. Baik, “Enhancing manufacturing product development through learning agent system over internet,” Comput. Ind. Eng., vol. 37, pp. 117–120, 1999. [81] K. Tumer and A. Agogino, “Improving air traffic management with a learning multiagent system,” IEEE Intell. Syst., vol. 24, no. 1, pp. 18–21, Jan./Feb. 2009. [82] C. Brunner, “IEC 61850 for power system communication,” in Proc. IEEE PES Transmiss. Distrib. Conf. Expo., 2008, pp. 1–6.

2372


[83] T. Strasser, A. Zoitl, J. H. Christensen, and C. Sunder, “Design and execution issues in IEC 61499 distributed automation and control systems,” IEEE Trans. Syst., Man Cybern., C: Appl. Rev., vol. 41, no. 1, pp. 41–51, Jan. 2011. [84] B. Bony, “Convergence of OPC UA and DPWS with a cross-domain data model,” in Proc. IEEE Int. Conf. Ind. Inf., 2011, pp. 187–192.

Paulo Leitão (M’98–SM’08) received the M.Sc. and Ph.D. degrees in electrical and computer engineering from the University of Porto, Porto, Portugal, in 1997 and 2004, respectively. From 1993 to 1999, he developed research activities at the CIM Center of Porto, and, from 1999 to 2000, at the Institute for Development and Innovation in Technology (IDIT). Since 2009, he has been with the Artificial Intelligence and Computer Science Laboratory (LIACC). He joined the School of Technology and Management, Polytechnic Institute of Bragança, Portugal, in 1995, and currently is Coordinator (Associate) Professor and Head of the Department of Electrical Engineering. He participates in several national and international research projects and Networks of Excellence, published more than 100 papers in high-ranked international scientific journals and conference proceedings (peer-review), coauthor of three patents, and served as Cochair of several international conferences, namely, IFAC IMS’10 and HoloMAS’11. His research interests are in the field of industrial informatics, collaborative factory automation, reconfigurable production systems, intelligent supervisory control, agent-based and holonic control, and bio-inspiration engineering. Dr. Leitão is Senior Member of IEEE Industrial Electronics Society (IES), and IEEE Systems, Man and Cybernetics Society (SMCS). He is currently the Chair of the IEEE Industrial Electronics Society Technical Committee on Industrial Agents.

Vladimír Mařík (M’95) received the Ph.D. and Dr.Sc. degrees from the Czech Technical University in Prague, Prague, Czech Republic, in 1979 and 1989, respectively. He is the Head of the Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague. He is author or coauthor of five monographs, eight textbooks, more than 140 papers at international conferences, 30 papers in reviewed journals, and coeditor of ten books. His main professional interests include distributed AI, multiagent systems, knowledge based systems, machine learning, and planning and scheduling for manufacturing. Prof. Mařík is the Editor-in-Chief of the IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS. He acted as a coordinator or a local coordinator of several international research projects in the field of AI (EUREKA, FP5, FP6, FP7). He was the General Chair of the DEXA’03, DEXA’93, IEEE/IFIP conference BASYS’98, BASYS’02, and cochair of the HoloMAS’03, ’05, ’07, ’09, and ’11, EUMAS’03, and IEEE DHMS’08 conferences and the IEEE DIS’06 workshop.

Pavel Vrba (M’05) received the Ph.D. degree in applied sciences and computer engineering from the University of West Bohemia, Pilsen, Czech Republic, in 2001. He is the head of Intelligent Systems Group at the Department of Cybernetics, Czech Technical University in Prague, Prague, Czech Republic. He is a Technical Lead of the Rockwell Automation Laboratory for Distributed Intelligent Control, Czech Technical University in Prague. He has published more than 60 conference papers, journal articles, and book chapters related to his research area and five pending/filed U.S. patents. His research interest includes applications of artificial intelligence methods, mainly multiagent systems, in various fields including smart grids, industrial automation, planning and scheduling, logistics, and others. Dr. Vrba was the Vice-Chair of the Technical Committee on Industrial Agents within IEEE Industrial Electronics Society and the Cochair of HoloMAS 2011 and InCom 2012 conferences.