Adaptive agents and multiagent systems

New information systems and recent applications (grid computing, Web Services, and so on) are often distributed, large-scale, open, heterogeneous, and characterized by a dynamic environment. To model these complex systems, researchers have spent much effort during the last few years on multiagent systems. The aim is to model complex distributed systems as a set of (possibly organized) software agents that interact in a common environment. The decomposition of a system into a number of agents lets the system react and adapt better in a changing environment. Moreover, organized structures ("social" structures) can emerge from interactions between agents, which in turn constrain and coordinate the agents' behavior. A multiagent system takes its metaphors of interaction from social systems rather than using the metaphor of the isolated thinker that early artificial intelligence researchers preferred. An important issue when dealing with this increasing complexity is to build adaptive agents and multiagent systems. Agents and multiagent systems must be aware of their own capabilities and of changes to other agents and their environment. To remain effective, agents must be able to adapt their structures and knowledge while they execute.

Adaptive Agents

The objective of multiagent systems is to find a solution to global problems or to control complex systems. These systems employ a set of agents that are autonomous, proactive, adaptive, and reactive.^{1, 2} Each agent has its own competencies and knowledge, but it needs to interact with other agents to solve complex problems, avoid conflicts, acquire and share information, and so on.