Multi-robot or Multi-agent

There are many papers/theses/journals I have read through come across the terms either “Multi-robot system” or “Multi-agent system” or both. Here, I want to do a parallel comparison of the two to make the terms clearer to me.

From above comparison we can see that “Multi-robot system” is mainly referred as the practical term, commonly used in robotics field; and the “Multi-agent system” is mainly referred as theoretical term, commonly used in computer science field.

REFERENCES
· Wikipedia, 2007, Multi-agent system
· YANG E.F. AND GU D.B., 2004, Multi-agent Reinforcement Learning for Multi-Robot Systems: A Survey
· CLARK, C.M., 2004, Dynamic robot networks: a coordination platform for multi-robot system
· ARAI T., PAGELLO E., PARKER L. E., 2002, Editorial: Advances in Multi-Robot Systems

BDI programming flow chart

Michael Wooldridge has mentioned four concrete Architectures for Intelligent Agents
1 Logic-based Architectures
2 Reactive Architectures
3 Belief-Desire-Intention Architectures
4 Layered Architectures

Here we focus on Belief-Desire-Intention (BDI) Architectures, which is the one being widely adopted and studied. I drafted the diagram blow after read Michael Wooldridge's book.

A systematical approach to understand BDI programming architecture, adoped from Michael Wooldridge's explanation.

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by Tom Atwood

Some impotant definations

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.