Multiagent systems and distributed artificial intelligence
1 / 38

Multiagent systems and Distributed Artificial Intelligence - PowerPoint PPT Presentation

  • Uploaded on

Multiagent systems and Distributed Artificial Intelligence. Agent?( 智能体). Agent: Intelligent Object Intelligent System with Only one agent A problem solving system by A algorithm or A* algorithm. Multiagent system. Intelligent System with two or more agents—Multiagent system

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about ' Multiagent systems and Distributed Artificial Intelligence' - kevyn

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript



Agent: Intelligent Object

Intelligent System with Only one agent

A problem solving system by A algorithm or A* algorithm

Multiagent system

Multiagent system

Intelligent System with two or more agents—Multiagent system

Game Playing System by alpha-beta procedure

Why multi agent system

Why Multi-agent system?

Difference between systems with one agent and multi-agents?

See an example.

An example boid

An example: Boid

Who designs and controls the behavior of Bird flocks, Fish schools?

See a computer model for computer simulation of the behavior of bird flocks, fish schools.

3 rules separation
3 rules: Separation

  • Separation: steer to avoid crowding local flockmates

3 rules alignment
3 rules: Alignment

  • Alignment: steer towards the average heading of local flockmates

3 rules cohesion
3 rules: Cohesion

  • Cohesion: steer to move toward the average position of local flockmates

Neighborhood around an agent
Neighborhood around an agent

  • Every agent reacts only to flockmates within a certain small neighborhood around itself.

  • The neighborhood is characterized by a distance and an angle,


  • The neighborhood is characterized by a distance (measured from the center of the boid) and an angle, measured from the boid's direction of flight.

  • Flockmates outside this local neighborhood are ignored.

  • The neighborhood could be considered a model of limited perception (as by fish in murky water)

Computer simulation to boids
Computer Simulation to Boids

  • three dimensional computational geometry of the sort normally used in computer animation or computer aided design.

  • See a demo by Java.

  • Sorry. Can not download it.

Obstacle avoidance
obstacle avoidance

  • Obstacle avoidance allowed the boids to fly through simulated environments while dodging static objects.

Demo available
Demo available?

  • See a demo

  • No Sorry here. 

What can we get from the example
What can we get from the example?

  • No Central controller.

  • Every agent: its behavior and the relationship to environments

  • Emergence(突现,涌现)

  • More examples of emergence.

History of multiagent systems
History of Multiagent systems

  • About late 1970s

  • Distributed Artificial Intelligence (DAI) evolved and diversified rapidly.

  • Research and application field. It brings together and draws on results, concepts, and idea from many disciplines: AI, computer science, sociology, economics, organization and management science, and philosophy.

Definition dai

  • DAI is the study, construction, and application of multiagent systems, that is, systems in which several interacting, intelligent agents pursue some set of goals or perform some set of tasks.

  • An agent is a computational entity such as a software program or a robot that can be viewed as perceiving and acting upon its environment and that is autonomous in that its behavior at least partially depends on its own experience.


  • An agent can be affected in its activities by other agents.

  • Agents try to combine their efforts to accomplish as a group what the individuals cannot in the case of cooperation.

  • Agents try to get what only some of them can have in the case of competition.

Why multiagent system 1
Why multiagent system?-1

  • Modern computing platforms and information environments are distributed, large, open, and heterogeneous.

  • These often exceed the level of conventional, centralized computing because they require processing of huge amounts of data, or of data that arises at geographically distinct locations.

Why multiagent system 2
Why multiagent system?-2

  • They have the capacity to play an important role in developing and analyzing models and theories of interactivity in human societies, and solving problems which it is difficult to solve in conventional method.

  • Many interactive processes among humans are still poorly understood, although they are an integreted part of our everyday life.(There are many things we do not know and we want to know related to multiagent systems)

Major characteristics of multiagent systems
Major characteristics of multiagent systems

  • Each agent has just incomplete information and is restricted in its capabilities.

  • System control is distributed;

  • Data is decentralized; and

  • Computation is asynchronous.

Reasons to study multiagent systems
Reasons to study multiagent systems

  • Technological and application needs:

    Offer a promising and innovative way to understand, manage, and use distributed, large-scale, dynamic, open, and heterogeneous computing and information systems.

  • Natural view of intelligent systems

Another example floys
Another example: Floys

  • flocking Artificial creatures.

  • with the social tendency to stick together

Two behavior rules
Two behavior rules

  • A rule specifying how to relate to one's own kind.

  • A rule specifying how to relate to strangers

How to relate to one s own kind
How to relate to one's own kind

  • Identify two members of your flock that are near to you and try to stay close to them, but not too close.

How to relate to strangers
How to relate to strangers

  • If you are in your territory: When you spot a stranger go after him, if you are close enough - attack

  • If you are not in your territory:If local Floys chase you - run away.

Rules of evolution 1
Rules of Evolution-1

  • eFloys evolve sexually, where each eFloy is the descendent of two parents.

  • Mother and father are selected according to the mechanism of 'Survival of the Fittest by Unnatural Selection'.

Rules of evolution 2
Rules of Evolution-2

  • Fitness is defined by two attributes, energy and safety.

  • If you are an eFloy, you can gain or lose these during your lifetime, and the more you have, the fitter you are

What influences fitness 1
What influences fitness?-1

  • Food is energy: each time you bite a stranger, your energy is increased. Your best option is to reach the stranger first, and eat him all by yourself.

  • If you are a stranger, each time you are bitten, your energy decreases.When your energy ends, you die.

What influences fitness 2
What influences fitness?-2

  • If you move fast, your energy decreases. The faster you move, the more energy you lose.

  • If you are close to your neighbors, your safety increases.The closer you are to your neighbors, the more safety points you get

A demo
A demo.

  • Wait please.

English books
English Books:

  • Artificial Intelligence: A new Synthesis, Nils J, Nilsson, 机械工业出版社,1999,9北京

  • Multiagent Systems: A modern approach to Distributed Artificial Intelligence, Edited by Gerhard Weiss, The MIT Press, Cambridge, Massachusetts, London, English.2000

A demo1
A demo.