After following through the first two lectures, I grabed some important concepts/definations which are keys to understand the Multiagent system. (Based on “An Introduction to MultiAgent Systems” by Michael Wooldridge, John Wiley & Sons, 2002.)
1. What is Agent?
An agent is a computer system that is capable of autonomous action on behalf of its user or owner in some environment in order to meet its design objectives.
An intelligent agent is a computer system capable of flexible autonomous action in some environment. By flexible, we mean: reactive, pro-active and social.
A static environment is one that can be assumed to remain unchanged except by the performance of actions by the agent. A dynamic environment is one that has other processes operating on it, and which hence changes in ways beyond the agent’s control.
2. Agent Control Loop
- Agent starts in some initial internal state i0.
- Observes its environment state e, and generates a percept see(e)Internal state of the agent is then updated via next function, becoming next(i0, see(e)).
- The action selected by the agent is action(next(i0, see(e)))
- Goto 2
3. What are the near/far linked fields?
The field of Multiagent Systems is influenced and inspired by many other fields:
Economics, Philosophy, Game Theory, Logic, Ecology and Social Sciences.
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The secretive French robotics company, Aldebaran Robotics , has released a sneek-peek of its secretive robotic project, Nao .
Project Nao, launched in early 2005, aims to make available to the public, at an affordable price, a humanoid robot with mechanical, electronic, and cognitive features, based on those of the prototype.
