Abstract
Multi-agent systems have been importantly contributing to the development of the theory and the practice of complex distributed systems and, in particular, have shown the potential to meet critical needs in high-speed, mission-critical content-rich and distributed information applications where mutual interdependencies, dynamic environments, uncertainty, and sophisticated control play a role. Therefore, multi-agent systems can be considered a suitable technology for the realization of healthcare applications where the use of loosely coupled and heterogeneous components, the dynamic and distributed management of data and the remote collaboration among users are often the most relevant requirements.
TopBackground
Agent and multi-agent system are terms that found their way into a number of technologies and they have been largely used in, e.g., Artificial Intelligence, Databases, Operating Systems and Computer Networks. Although there is no such thing as an accepted definition of an agent (see, e.g., Genesereth & Ketchpel, 1994; Wooldridge & Jennings, 1995; Russell & Norvig, 2003), all definitions agree that an agent is essentially an autonomous software entity that provides an interoperable interface and that behaves like a rational actor working on behalf of some client in pursuit of its own agenda. Agents are designed to operate in dynamic and uncertain environments, making complex decisions at run-time, and the learning capabilities of some kinds of agents make them able to improve their performances over time, thus avoiding repeated negative conditions and persisting on successful behaviours.
Even if a complex system can be based on a solitary agent working within its environment–that may or may not comprise users–usually agent-based systems are realized in terms of multiple, interacting agents, i.e., agent-based systems are normally multi-agent systems.
Multi-agent systems are generally considered an appropriate means for modelling complex, distributed systems, even if such a multiplicity naturally introduces the possibility of having different agents with potentially conflicting goals. Agents may decide to cooperate for mutual benefit, or they may compete to serve their own interests. Agents take advantage of their social ability to exhibit flexible coordination behaviours that make them able to both cooperate in the achievement of shared goals or to compete on the acquisition of resources and tasks. Agents have the ability of coordinating their behaviours into coherent global actions. Coordination among agents can be handled with a variety of approaches including, negotiation, contracting, organizational structuring and multi-agent planning.
Key Terms in this Chapter
Multi-Agent System: A multi-agent system (MAS) is a loosely coupled network of software agents that interact to solve problems that are beyond the individual capacities or knowledge of each software agent.
Multi-Agent Planning: A process that can involve agents plan for a common goal, agents coordinating the plan of others, or agents refining their own plans while negotiating over tasks or resources.
Negotiation: A process by which a group of agents come to a mutually acceptable agreement on some matter.
Coordination: Coordination is a process in which a group of agents engages in order to ensure that each of them acts in a coherent manner.
Contracting: A process where agents can assume the role of manager and contractor and where managers try to assign tasks to the most appropriate contractors.
Software Agent: A software agent is a computer program that is situated in some environment and capable of autonomous action in order to meet its design objectives.
Organizational Structuring: A process for defining the organizational structure of a multi-agent system, i.e., the information, communication, and control relationships among the agents of the system.