Industrial Agents

Emerging Applications of Software Agents in Industry

Paulo Leitão, Stamatis Karnouskos

• Publisher: Elsevier
• Year: 2015
• Print ISBN: 9780128003411
• eBook ISBN: 9780128004111
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Industrial Agents explains how multi-agent systems improve collaborative networks to offer dynamic service changes, customization, improved quality and reliability, and flexible infrastructure. Learn how these platforms can offer distributed intelligent management and control functions with communication, cooperation and synchronization capabilities, and also provide for the behavior specifications of the smart components of the system. The book offers not only an introduction to industrial agents, but also clarifies and positions the vision, on-going efforts, example applications, assessment and roadmap applicable to multiple industries. This edited work is guided and co-authored by leaders of the IEEE Technical Committee on Industrial Agents who represent both academic and industry perspectives and share the latest research along with their hands-on experiences prototyping and deploying industrial agents in industrial scenarios. - Learn how new scientific approaches and technologies aggregate resources such next generation intelligent systems, manual workplaces and information and material flow system - Gain insight from experts presenting the latest academic and industry research on multi-agent systems - Explore multiple case studies and example applications showing industrial agents in a variety of scenarios - Understand implementations across the enterprise, from low-level control systems to autonomous and collaborative management units

Part I

Industrial Agents: Concepts and Definitions

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

Abstract

Agents and multi-agent systems are one of the most fascinating topics in computer science. They attracted and unified not only researchers from nearly all computer science areas but also researchers from other core disciplines such as psychology, sociology, biology, or control engineering. In the meantime, agent-based systems successfully prove their usefulness in many different real-life application areas, especially industrial ones. This is a clear sign that this discipline has become mature. This chapter presents a comprehensive state-of-the-art introduction into advanced software agents and multi-agent systems. Properties and types of agents and multi-agent systems are discussed, which include precise definitions of both. A successful cooperation between agents is only possible if they can communicate in an efficient and semantically meaningful way. Thus, relevant communication strategies are discussed. Agent-based applications can be very powerful, complex systems. Their development can profit a lot from adequate support tools. Different development support options and environments are discussed in some detail. Due to their nature, multi-agent systems are excellent candidates for the realization of comprehensive simulations, especially if the individuality and uniqueness of components of the simulation environment play an important role. The second part of the chapter addresses supporting technologies and concepts. Ontologies, self-organization and emergence, and swarm intelligence and stigmergy are introduced and discussed in some detail.

Keywords

Software agents

Multi-agent systems

Ontologies

Self-organization

Emergence

Swarm intelligence

Stigmergy

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, especially 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 technologies, 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 development 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.

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 the agent wants to achieve in a specific situation. They may only express what the agent can do in this specific situation at most, and so usually only have a temporary character. As Logan and Scheutz (2001) state, deliberativeness is often realized by applying the concept of symbolic representation with compositional semantics (e.g., data tree) in all major functions, for an agent’s deliberation is not limited to presenting facts, but to construe hypotheses about possible future states, and in doing so, potentially offer information about the past. These hypothetical states involve goals, plans, partial solutions, hypothetical states of the agent’s beliefs, etc. On top of its symbolic representation, a deliberative agent has methods to interpret and predict the outside world in order to compare its state to the agent’s desired state (goal). On the basis of these interpretations and assumptions, it develops the best possible plan (from its point of view) and executes it. Intelligent planning is a complex process, especially if the resulting plan is comparatively sophisticated and spans a large exponentially growing solution space. During this planning time, the environment may change in a way that makes the execution of the actual plan (partially) obsolete or suboptimal. Thus, an immediate re-planning may be necessary. Vlahavas and Vrakas (2004) believe that deliberative agents are especially useful when a reasonable reaction to a sophisticated situation is required (however, not a real-time one), because of their ability to produce high-quality, domain-independent solutions.

While deliberative agents are comparatively flexible in acting upon their environment, they may, on the other hand, become considerably complex and grow slow in their reactions. The architecture and behavior of a reactive agent are simpler because the agent doesn’t have to deal with a representation of a symbolic world model, nor does it utilize complex symbolic reasoning. Instead, reactive behavior implies that the agent responds comparatively quickly to relevant stimuli from its environment. Based on this input, it produces output by simple situation-action associations, usually implemented via pattern matching. Reactive agents need few resources, and so can ...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Preface
• List of Contributors
• Part I: Industrial Agents: Concepts and Definitions
• Part II: Industrial Agents: Related Concepts and Technologies
• Part III: Industrial Agent Applications
• Part IV: A Survey on Factors that Impact Industrial Agent Acceptance
• Reference Index
• Author Index
• Subject Index