7 - Indira Gandhi Centre for Atomic Research

complete the task successfully. It should be able to change itself based on changes
occurring in its environment, so that a change in circumstances will still yield the
intended result’.
An agent is characterized by the following properties:
• Autonomy: Operation without the direct intervention of humans or others, and
have some kind of control over their actions and internal state.
• Social ability: Interaction or communication with other agents
• Reactivity: Perception of their environment and response in a timely fashion to
changes that occur in it.
• Pro-activity: Response to their environment and exhibit goal-directed behavior by
taking the initiative. An agent is capable of handling complex, high-level tasks.
The decision as to how such a task is best split up into smaller sub-tasks.
• Temporal continuity: agents are continuously running processes.
• Mobility: the ability to transport itself from one machine to another, retaining its
current state.
• Anatomy: Similar anatomy as that of objects. However It’s state and behavior is
expressed differently.
The agent’s execution model contains:
• Static knowledge on itself and other agents (acquaintances).
• Expertise knowledge the agent represents, which can be described in various forms
such as production rules, frames, logical expressions, etc.
• Reasoning: the inferences which draw the problem resolution.
• Communication; the communication protocols between the agents.
• Cooperation strategies used by the agents to cooperate with others.
As MAS are usually made up of large number of independently designed software
components (agents), it is very difficult to exactly design and handle the system as a
whole. It’s not enough to simply put components together and let them interact. Rather a
systematic approach is needed focusing on the role each agent plays in the system, the
mechanisms upon which composition can be based, and their composition laws. A more
dynamic and complex environment, also termed as agent space, is required which supports
a complex communication among agents. A software architecture, which is interactionoriented,
rather than composition-oriented is needed
The agents in multi agent systems work in a team to achieve common goals by interacting
with each other. Several researchers put their efforts in the areas related to agent
communication like agent language, architecture, distribution strategies etc. But these
efforts focus on the agents’ internal structure where as a comprehensive view of a multiagent
system should not rely only upon the analysis of the internal behavior of each agent,
due to their intrinsically interactive nature. In fact, these systems are likely to exhibit a
complex global behavior, emerging from the mutual interaction among components, that is
hard to be described and managed when communication is considered from a single
agent’s viewpoint. In multi-agent systems a team effort leads to a social behavior.
Therefore, to fully understand multi-agent systems, theories for agent societies are needed.
These theories should define what is the world that hosts the society, which laws rule the
world, and which are the individuals that can populate it. In addition, if any intelligent
global behavior can emerge from a system, there should be a place where it should be
found and monitored. In a multi-agent world, this intelligence cannot reside inside agents
only, but it should be somehow spread among agents and the interaction space among
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