Intelligent MAS in System Engineering and Robotics

Intelligent MAS in System Engineering and Robotics

G. Nicolás Marichal (University of La Laguna, Spain) and Evelio J. González (University of La Laguna, Spain)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-59904-849-9.ch135
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Abstract

The concept of agent has been successfully used in a wide range of applications such as Robotics, e-commerce, agent-assisted user training, military transport or health-care. The origin of this concept can be located in 1977, when Carl Hewitt proposed the idea of an interactive object called actor. This actor was defined as a computational agent, which has a mail address and a behaviour (Hewitt, 1977). Actors receive messages from other actors and carry out their tasks in a concurrent way. It is difficult that a single agent could be sufficient to carry out a relatively complex task. The usual approach consists of a society of agents - called Multiagent Systems (MAS) -, which communicate and collaborate among them and they are coordinated when pursuing a goal. The purpose of this chapter is to analyze the aspects related to the application of MAS to System Engineering and Robotics, focusing on those approaches that combine MAS with other Artificial Intelligence (AI) techniques.
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Background

There is not an academic definition accepted by every researcher about the term agent. In fact, agent researchers have offered a variety of definitions explicating his or her particular use of the word. An extensive list of these definitions can be found in (Franklin and Graesser, 1996). It does not fall in the scope of this chapter to reproduce that list. However, we will include some of them, in order to illustrate how heterogeneous these definitions are.

“Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed.” (Maes, 1995, p. 108)
“Autonomous agents are systems capable of autonomous, purposeful action in the real world.” (Brustoloni, 1991, p. 265)
“An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.” (Russell and Norvig, 1995, p. 31)

Despite the existing plethora of definitions, agents are often characterized by only describing their features (long-live, autonomy, reactivity, proactivity, collaboration, ability to perform in a dynamic and unpredictable environment, etc.). With these characteristics, users can delegate to agents tasks designed to be carried out without human intervention, for instance, as personal assistants that learn from its user.

In most of applications, a standalone agent is not sufficient for carrying out the desired task: agents are forced to interact with other agents, forming a MAS. Due to their capacity of flexible autonomous action, MAS can treat with open – or at least highly dynamic or uncertain- environments. On the other hand, MAS can effectively manage situations where distributed systems are needed: the problem being solved is itself distributed, the data are geographically distributed, systems with many components and huge content, systems with future extensions, etc. A researcher could include a single agent to implement all the tasks. Nevertheless, this type of macroagent represents a bottleneck for the system speed, reliability and management.

It is clear that the design of a MAS is more complex than a single agent. Apart from the code for the treatment of the task-problem, a developer needs to implement those aspects related to communication, negotiation among the agents and its organization in the system. Nevertheless, it has been shown that MAS offer more than they cost (Cockburn, 1996) (Gonzalez, 2006) (Gonzalez, 2006b) (Gyurjyan, 2003) (Seilonen 2005).

Key Terms in this Chapter

Ontology: Set of classes, relations, functions, etc. that represents knowledge of a particular domain

Artificial Neural Network: An organized set of many simple processors called neurons that imitates a biological neural configuration.

Neuro-Fuzzy: Hybrids of Artificial neural networks and Fuzzy Logic.

FIPA: It stands for “Foundation for Intelligent Physical Agents”, IEEE Computer Society standards organization that promotes agent-based technology and the interoperability of its standards with other technologies

MultiAgent System: System composed of several agents, usually designed to cooperate in order to reach a goal

Real-Time System: System with operational deadlines from event to system response.

Self-Tuning Regulator: Type of adaptive control system composed of two loops, an inner loop (process and ordinary linear feedback regulator), and an outer loop (recursive parameter estimator and design calculation which adjusts its parameters)

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