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Agent Technology in Modern Manufacturing

1.1 Introduction

With the development of internet, computer, management, and manufacturing technologies, the manufacturing industry is undergoing a huge transformation from traditional manufacturing to agile manufacturing, networked manufacturing, virtual manufacturing, service-based manufacturing, and cloud manufacturing. These new manufacturing systems are characterized by smartness, integration, and flexibility, and can be well described as Agent technology. The cooperation and communication of multiple agents can be adopted to improve the performance of manufacturing systems.

1.2 Agent and Multi-Agent System

Research and application of Agent technology stem from a series of studies on distributed artificial intelligence conducted by MIT researchers in the 1970s.[1] Distributed artificial intelligence mainly focuses on solving distributed agent problems. There are two important branches:[2] distributed problems and Multi-Agent Systems (MASs). The distributed problems were conducted at an early stage in the distributed artificial intelligence area. The distributed problems have been extended to Multi-Agent Systems. The Multi-Agent System is a system with Agents of different abilities to complete collaboratively certain tasks or achieve certain objectives.[3-5]

1.2.1 Agent

The concepts, properties, and research methods of Agent technology are developed from artificial intelligence. It is difficult to define either artificial intelligence or Agent. Many different definitions have been given by different schools for different requirements. The earliest concept of Agent was defined based on the concurrent actor model proposed by Hewitt in the early 1970s.[6] In the concurrent actor model, Hewitt defined a term-actor with the characteristics of self-organization, interaction, and parallel execution. The most classic and widely accepted definition was given by Wooldridge, et al.[7] The definition contains "weak definition" and "strong definition". The weak definition defines an Agent as a hardware and software system with autonomous ability, social skill, and responsive and predictive ability; the strong definition includes the properties of the weak definition and also the properties of knowledge, mobility, veracity, rationality, and so on.

Computer science researchers[8] consider that an Agent is a computer system based on software and hardware; it also has autonomy reactivity, socialability, proactiveness, and other properties. From the perspective of the evolution of software design methods, agent-based software engineering methods are proposed on the basis of object-oriented software engineering methods. Moreover, decomposition and abstraction methods of complex software systems, distributed computing capabilities, interactive coordination mechanism, calculation model, and software architecture have been proposed.

Researchers in artificial intelligence are more inclined to a narrow point of view, except for the above properties. It is therefore necessary to give a more specific meaning for an Agent. Terms such as belief, intention, and commitment are used to describe an Agent. An Agent tries to mimic a human's thinking and intelligent behavior: for example, what the Agent is doing, what the Agent knows, what the Agent wants, and so on. This definition is developed on the basis of AI knowledge symbols. Shoham[9] thought an Agent was a symbolic reasoning system, which contained the expression of symbols on environment and expected behavior.

Therefore, an Agent is an intelligent individual. Wooldridge and Jennings[7] proposed that an Agent should have four basic attributes: autonomy, reactivity, social ability, and initiative. Sargent[10] considered that the most basic attributes of an Agent were reactivity, autonomy, goal-orientation, and environmental resistance. An Agent was defined by Muller[11] as follows: 1) it is necessary to have other Agents and a virtual world where an Agent exists; 2) an Agent can perceive a virtual world and influence the virtual world; 3) an Agent can at least partly represent the virtual world; 4) an Agent is target-oriented and has the ability to arrange its own activities; 5) an Agent can communicate with other Agents. Most researchers think that an Agent should not only meet basic properties, but should also have other properties according to application requirements: for example, mobility, learning and adaptability, interactivity, planning ability, rationality, persistent or time continuity, and so on. Three directions of current research are intelligence, agency, and mobility.[12] From the intelligence point of view, an Agent is an expert system; agency means that an Agent can be used to represent the role of a man and machine; while mobility means that an Agent can move or run on a different machine on the internet.

As the previous presentation demonstrates, an Agent should have the following properties:[13-21]

1. Autonomy: An Agent can control its behavior and internal state by itself, and it cannot be controlled by others. This is used to differentiate an Agent with other concepts such as process and object.
2. Reactivity: An Agent can feel the environment and respond appropriately to environment-related events.
3. Sociality: An Agent is in a social environment constituted by multiple Agents. These Agents exchange information with each other in some interactive methods. These Agents collaborate with each other to solve different problems and help other Agents complete related activities. Agents exchange information by a communication language.
4. Initiative: The reaction of an Agent to the environment is a goal-directed initiative behavior. In some cases, the behavior of the Agent is triggered by its own requirements. The reactive behavior is a kind of positive behavior or an active communication with the environment.
5. Adaptability: An Agent can respond to environmental changes, adopt a goal-oriented action at the appropriate time, and learn from its own experience, the environment, and the interaction process with other Agents.
6. Interoperability: An Agent can work with other Agents to complete complex tasks, which is a social behavior.
7. Learning ability: An Agent can learn from the surrounding environment and cooperative experiences so as to improve its own capability.
8. Evolutionary development: An Agent can improve itself through learning, and reproduce and follow Darwin's natural selection rule "survival of the fittest".
9. Honesty: An Agent does not intend to deceive users.
10. Rationality: the action taken by an Agent and its consequences will not harm its own interest and other Agents' interests.
11. Persistence: An Agent is ongoing, not temporary, its status should be consistent, which is not in contradiction with property (8).
12. Mobility: An Agent should have the ability to move independently in the network, while its status remains unchanged.
13. Reasoning: An Agent can reason and forecast in a rational manner according to accumulated past knowledge, states of the current environment, and other Agents.
14. Others: philanthropic, adventurous or conservative, helpful or hostile, and so on.

The above attributes show that an Agent is similar to a person, which provides a new method for solving complex problems in computer science and artificial intelligence. Although an Agent may have a variety of properties, researchers and developers do not need to develop one Agent or an Agent system with all the attributes. Agents with several attributes and Multi-Agent systems with several attributes are developed according to actual requirements.

1.2.2 Multi-Agent System

Agent Systems can be classified into two classes: Single-Agent Systems and Multi-Agent Systems (MASs). The research of a Single-Agent System focuses on simulating human intelligent behavior; it concentrates on investigating human intelligent behavior such as computing ability, reasoning ability, memory, learning ability and intuition, and so on. The research of a MAS focuses on the collaborative process among autonomous intelligent Agents that generate their corresponding behaviors or solve problems by coordinating Agent goals and planning Agents. In the problem-solving process, these Agents share all their knowledge about related problems and methods in order to achieve a global objective, or their own local objectives.[22-25]

As regards a MAS, a computing system aims to complete collaboratively certain tasks or achieve objectives by some Agents. The system consists of multiple autonomous or semi-autonomous Agents.[26] In a MAS, each Agent cooperates with other Agents to complete a complex task that cannot be solved by single Agent. All the Agents are autonomous, running in a distributed mode, or even heterogeneous. The subroutine, function, or process of each Agent are different, its goal and behavior are relatively autonomous and independent. Each Agent cooperates with other Agents in order to deal with conflict among them. A MAS has advantages of traditional distribution concurrent problem, and it runs in an interactive communication mode. Compared with a single Agent, each Agent in a MAS has incomplete information and it is able to solve problems, the data is...