Industrial Agents: Emerging Applications of Software Agents in Industry

446 Pages·2015·37.01 MB·English

by  Paulo Leitão

Industrial Agents
Emerging Applications of Software
Agents in Industry
Industrial Agents
Emerging Applications of Software
Agents in Industry
Edited by
Paulo Leitão
Stamatis Karnouskos
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Preface
The objective of this book is to address both industry practitioners and academia, providing the vision,
on-going efforts, example applications, assessments, and roadmaps associated with industrial agents
used in multiple industries. Such a book provides an introduction to the “industrial agents” domain
by discussing up-to-date examples of their applications in industry, and it offers a view of future chal-
lenges with an accompanying roadmap.
Part I introduces industrial agents, as well as the benefits, limitations, and applicability of agent
technology, and it considers competing and complementary approaches for designing, deploying, and
assessing industrial agent systems.
Part II discusses related concepts and technologies that are complementary to the implementation of
the agent technology, namely service orientation, integration with low-level controls using IEC6113-3
and IEC 61499 standards, resilience and security, and the requirements for the application of industrial
agents in virtual enterprises and at production automation levels.
Part III provides a catalog of industrial agent-based applications, considering different sectors. Each
chapter describes an existing industrial application or an innovative future application currently being
developed in cutting-edge R&D projects. This catalog is structured around motivation/overview, de-
tailed application description, benefits, assessment, and conclusion.
Finally, Part IV provides a survey analysis identifying the factors that impact the industrial accep-
tance of this paradigm and the market and application domains that better benefit from using agents.
This part finishes with a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis for the
application of agent technology in industrial environments.
xv
List of Contributors
Juan L. Asenjo
Customer Support and Maintenance, Rockwell Automation, Cleveland, OH, USA
José Barata
Uninova–CTS, Departamento de Engenharia Electrotécnica, Faculdade de Ciências e Tecnologia,
FCT, Universidade Nova de Lisboa, Caparica, Portugal
Federico Bergenti
Dipartimento di Matematica e Informatica, Università degli Studi di Parma, Parma, Italy
Giovanni Caire
Telecom Italia S.p.A., Torino, Italy
Sergio Cavalieri
CELS, Università degli studi di Bergamo, Bergamo, Italy
Armando Walter Colombo
University of Applied Sciences Emden/Leer, Emden, Germany; Schneider Electric Automation
GmbH, Marktheidenfeld, Germany
Hossein Davari Ardakani
NSF I/UCRC Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati,
OH, USA
Christian Derksen
Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-
Essen, Essen, Germany
Amro Farid
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA,
USA; Masdar Institute of Science and Technology, Abu Dhabi, UAE
Luca Fasanotti
CELS, Università degli studi di Bergamo, Bergamo, Italy
Alexander Fay
Helmut Schmidt University, Hamburg, Germany
Matthias Foehr
Siemens AG, Corporate Technology, Erlangen, Germany
Jens Folmer
Institute of Automation and Information Systems, Technische Universität München, Munich,
Germany
Peter Göhner
Institute of Industrial Automation and Software Engineering, University of Stuttgart, Stuttgart,
Germany
xvii
xviii List of Contributors
Danilo Gotta
Telecom Italia S.p.A., Torino, Italy
Reinhard Grabler
Practical Robotics Institute Austria, Vienna, Austria
Benjamin Groessing
Vienna University of Technology, Vienna, Austria
Johannes Hoos
Festo AG & Co. KG, Esslingen, Germany
Stefano Ierace
CELS, Università degli studi di Bergamo, Bergamo, Italy
Hung-An Kao
NSF I/UCRC Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati,
OH, USA
Stamatis Karnouskos
SAP, Karlsruhe, Germany
Thomas Konnerth
Technische Universität Berlin, DAI-Labor, Berlin, Germany
Gottfried Koppensteiner
Vienna University of Technology, Vienna, Austria; Practical Robotics Institute Austria, Vienna,
Austria
Tobias Küster
Technische Universität Berlin, DAI-Labor, Berlin, Germany
Jay Lee
NSF I/UCRC Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati,
OH, USA
Christoph Legat
Institute of Automation and Information Systems, Technische Universität München, Munich,
Germany
Paulo Leitão
Polytechnic Institute of Bragança, Bragança, Portugal; LIACC—Artificial Intelligence and Computer
Science Laboratory, Porto, Portugal
Wilfried Lepuschitz
Vienna University of Technology, Vienna, Austria; Practical Robotics Institute Austria, Vienna,
Austria
Tobias Linnenberg
Helmut Schmidt University, Hamburg, Germany
Arndt Lüder
Institute of Ergonomics, Manufacturing Systems and Automation, Otto von Guericke University,
Magdeburg, Germany
List of Contributors xix
Marco Lützenberger
Technische Universität Berlin, DAI-Labor, Berlin, Germany
Francisco P. Maturana
Common Architecture and Technology, Rockwell Automation, Cleveland, OH, USA
João Marco Mendes
Schneider Electric Automation GmbH, Marktheidenfeld, Germany
Munir Merdan
Austrian Institute of Technology, Vienna, Austria; Practical Robotics Institute Austria, Vienna,
Austria
David P. Miller
University of Oklahoma, Norman, OK, USA
Mauro Onori
EPS Group, Department of Production Engineering, Kungliga Tekniska Högskolan, Stockholm,
Sweden
Arnaldo Pagani
Whirlpool Europe, Cassinetta di Biandronno, Italy
Carlos Eduardo Pereira
Electrical Engineering Department, Federal University of Rio Grande do Sul, Porto Alegre,
Brazil
Luis Ribeiro
Department of Management and Engineering (IEI), Division of Manufacturing Engineering,
Linköping University, Linköping, Sweden
Sergio Rivera
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge,
MA, USA
Nelson Rodrigues
Polytechnic Institute of Bragança, Bragança, Portugal; LIACC—Artificial Intelligence and Computer
Science Laboratory, Porto, Portugal
Daniel Schütz
Institute of Automation and Information Systems, Technische Universität München, Munich,
Germany
David Siegel
NSF I/UCRC Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati,
OH, USA
Petr Skobelev
Smart Solutions, Ltd., Samara State Aerospace University, Samara, Russia
Thomas Strasser
AIT Austrian Institute of Technology GmbH, Vienna, Austria
Claudio Turrin
Whirlpool Europe, Cassinetta di Biandronno, Italy
xx List of Contributors
Rainer Unland
Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-
Essen, Essen, Germany; Department of Computer Science and Software Engineering, University
of Canterbury, Christchurch, New Zealand
Birgit Vogel-Heuser
Institute of Automation and Information Systems, Technische Universität München, Munich,
Germany
Kamal Youcef-Toumi
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA,
USA
Alois Zoitl
Fortiss GmbH, Munich, Germany
Marcos Zuccolotto
Electrical Engineering Department, Federal University of Rio Grande do Sul, Porto Alegre,
Brazil
CHAPTER
1
SOFTWARE AGENT
SYSTEMS
Rainer Unland
Institute for Computer Science and Business Information Systems (ICB),
University of Duisburg-Essen, Essen, Germany;
Department of Computer Science and Software Engineering,
University of Canterbury, Christchurch, New Zealand
1.1 INTRODUCTION
In the beginning of the 1990s, agents and agent-based systems started to become a major research topic.
Very soon, they became one of the hottest and most-funded research topics in computer science. One
of the fascinating facets of agent-based research has always been that it attracted not only researchers
from most computer science areas but also researchers from other core research disciplines, such as
psychology, sociology, biology, and control engineering. Of course, these huge influences from many
sides led to some chaotic and hardly controllable research. Since then, the tempest has calmed and
agent-based systems have slowly found their way into real-life applications in many disciplines, espe-
cially industrial ones. This is a clear sign that this discipline has started to become mature.
This chapter will offer a general introduction of agents, agent-based systems, and related technolo-
gies, but will be slightly influenced by the view and requirements of industrial applications. Thus, the
remainder of this chapter is organized as follows. The next section discusses the fundamentals of agents
and agent-based systems, and will especially discuss the set of properties associated with them. Also,
different kinds of agent communication will be introduced. The section closes with a discussion of de-
velopment concepts for agent-based systems. Section 1.2.6 presents technologies and concepts closely
related to, and that substantially extend, the capabilities of agent technology. In particular, ontologies,
self-organization and emergence, and swarm intelligence and stigmergy are discussed in more detail.
Finally, Section 1.4 offers a summary of these developments.
1.2 FUNDAMENTALS OF AGENTS AND AGENT-BASED SYSTEMS
1.2.1 AGENTS AND AGENT PROPERTIES
An agent can be regarded as an autonomous, problem-solving, and goal-driven computational entity
with social abilities that is capable of effective, maybe even proactive, behavior in an open and dynamic
environment in the sense that it is observing and acting upon it in order to achieve its goals (cf., e.g.,
Wooldridge and Jennings, 1995; Wooldridge, 2002). There are a number of definitions of intelligent
agents that need to be extended in the light of long successful research in this area (cf., e.g., Weiss,
1999; Object Management Group, 2004). The set of features that is to be supported when the term
(advanced) agent is used encompasses the properties listed in Table 1.1.
3
4 CHAPTER 1 SOFTWARE AGENT SYSTEMS
Table 1.1 Properties of (Advanced) Agents
Autonomy: An intelligent agent has control over its behavior (i.e., it operates without the direct intervention of
human beings or other entities from the outside world). It has sole control over its internal state and its goals and is
the only instance that can change either
Responsiveness/situatedness: An agent is equipped with sensors and actuators, which form its direct interface to
its environment. It perceives its environment by receiving sensory inputs from it. It responds in a timely manner
to relevant changes in it through its actuators. The reaction reflects its design goals in the sense that it always tries
to steer toward these goals
Proactiveness: A more sophisticated agent acts not only responsively but may even be opportunistic and act on
initiative (i.e., it may proactively anticipate possible changes in its environment and react to them)
Goal-orientation: An intelligent agent is goal-directed. This implies that it takes initiative whenever there is an
opportunity to work toward its goals
Smart behavior: An agent has comprehensive expertise and knowledge in a specific, well-defined area. Thus, it is
capable of dealing with and solving problems in this domain. The most common may be equipped with an internal
representation of that part of the world it has to act in
Social ability: An agent interacts directly with humans and/or other agents in pursuit of its individual, organizational,
and/or combined goals. Especially, more intelligent agents may have to deal with all kinds of (unpredictable)
situations in which they may need help from other agents. Thus, they may collect and maintain knowledge about
other agents (their contact, (subjective) capabilities, reliability, trustworthiness, etc.) and their acquaintances’
information
Learning capabilities: In order for agents to be adaptive and autonomous, they need to able to learn without
intervention from the outside. According to Maes (1994), learning is meant to be incremental, has to take the noise
into account, is unsupervised, and can make use of the background knowledge provided by the user and/or the
developer of the system
1.2.2 TYPES OF AGENTS
Agent research defines deliberative and reactive agents as the extreme points within the spectrum for
the smartness of agents.
Depending on the point of view, a deliberative, respectively (cognitive) intentional agent is either
a synonym for a proactive agent or a specialization of it. Its behavior and architecture is reasonably
sophisticated (i.e., the internal processes and computation is comparatively complex and, thus, time-
and resource-consuming. However, in contrast to human beings, an agent “understands” at most only
a small, abstracted portion of the real world, although it has always been intended to equip it with
comprehensive real-world knowledge. This goal was in the mind of researchers from the beginning,
but up to now has turned out to be too ambitious. Wooldridge (1995) defines a deliberative agent as
one “that possesses an explicitly represented, symbolic model of the world, and in which decisions
(e.g., about what actions to perform) are made via symbolic reasoning.” The most popular architecture
for the implementation of such agents is the belief-desire-intention architecture (BDI) (cf. Bratman,
1987). The beliefs reflect the agent’s abstract understanding of that comparatively small part of the real
world it is an expert in. This understanding is subjective to the agent, and thus may vary from agent
to agent. The desires represent the goals of the agent (i.e., describe what the agent wants to achieve).
It can be distinguished between short-term goals and long-term goals. The long-term goals are those
that actually drive the behavior of an agent, and thus are comparatively stable and abstract. They form
the underlying decision base for all (re)actions of the agent. Short-term goals only reflect goals that